Yash Vardhan Pant | publications

Accepted papers

2022

Stable Interaction of Autonomous Vehicle Platoons with Human-Driven Vehicles

Pirani, Mohammad, She, Yining, Tang, Renzhi, Jiang, Zhihao, and Pant, Yash Vardhan

In American Control Conference (ACC) 2022

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A necessary prerequisite for the safe interaction of autonomous systems with a human-driven vehicle is for the overall closed-loop system (autonomous systems plus human-driven vehicle) to be stable. This paper studies the safe and stable interaction between a platoon of autonomous vehicles and a set of human-driven vehicles. Considering the longitudinal motion of the vehicles in the platoon, the problem is to ensure a safe emergency braking by the autonomous platoon considering the actions of human-driven vehicles, which may vary based on the driver type. We consider two types of platoon topologies, namely unidirectional and bidirectional. Safe emergency braking is characterized by a specific type of platoon stability, called head-to-tail stability (HTS). We present system-theoretic necessary and sufficient conditions for the combination of the autonomous platoon and human-driven vehicles to be HTS for two platoon control laws, namely the velocity tracking and the platoon formation. Modeling the input-output behavior of each vehicle via a transfer function, the HTS conditions restrict the human-driven vehicles’ transfer functions to have H_∞ norms below certain thresholds. A safe interaction algorithm first identifies the transfer functions of the human-driven vehicles. Then, it tunes the platoon control gains such that the overall system meets HTS conditions. Theoretical results are validated with both experimental data with human subject studies and simulation studies.

@inproceedings{pirani2022acc, author = {Pirani, Mohammad and She, Yining and Tang, Renzhi and Jiang, Zhihao and Pant, Yash Vardhan}, title = {{Stable Interaction of Autonomous Vehicle Platoons with Human-Driven Vehicles}}, booktitle = {American Control Conference (ACC)}, year = {2022}, img = {ACC2022.png} }

2021

Addressing the IEEE AV test challenge with Scenic and VerifAI

Viswanadha, Kesav, Indaheng, Francis, Wong, Justin, Kim, Edward, Kalvan, Ellen, Pant, Yash, Fremont, Daniel J, and Seshia, Sanjit A

In 2021 IEEE International Conference on Artificial Intelligence Testing (AITest) 2021

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This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems. First, to model and generate interactive scenarios involving multiple agents, we used SCENIC, a proba bilistic programming language for specifying scenarios. A SCENIC program defines an abstract scenario as a distribution over configurations of physical objects and their behaviors over time. Sampling from an abstract scenario yields many different concrete scenarios which can be run as test cases for the AV. Starting from a SCENIC program encoding an abstract driving scenario, we can use the VERIFAI toolkit to search within the scenario for failure cases with respect to multiple AV evaluation metrics. We demonstrate the effectiveness of our testing framework by identifying concrete failure scenarios for an open-source autopilot, Apollo, starting from a variety of realistic traffic scenarios.

@inproceedings{viswanadha2021addressing, title = {Addressing the IEEE AV test challenge with Scenic and VerifAI}, author = {Viswanadha, Kesav and Indaheng, Francis and Wong, Justin and Kim, Edward and Kalvan, Ellen and Pant, Yash and Fremont, Daniel J and Seshia, Sanjit A}, booktitle = {2021 IEEE International Conference on Artificial Intelligence Testing (AITest)}, pages = {136--142}, year = {2021}, organization = {IEEE}, img = {AITest2021.png} }
DEC-LOS-RRT: Decentralized path planning for multi-robot systems with Line-of-sight constrained communication

Tuck, Victoria, Pant, Yash Vardhan, Seshia, Sanjit A., and Sastry, S Shankar

In 2021 IEEE Conference on Control Technology and Applications (CCTA) 2021

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Decentralized planning for multi-agent systems,such as fleets of robots in a search-and-rescue operation, is oftenconstrained by limitations on how agents can communicate witheach other. One such limitation is the case when agents cancommunicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods thatguarantee safety is difficult in this case, as agents that areoccluded from each other might not be able to communicateuntil it’s too late to avoid a safety violation. In this paper, wedevelop a decentralized planning method that explicitly avoidssituations where lack of visibility of other agents would leadto an unsafe situation. bxuilding on top of an existing Rapidly-exploring Random Tree (RRT)-based approach, our methodguarantees safety at each iteration. Simulation studies showthe effectiveness of our method and compare the degradationin performance with respect to a clairvoyant decentralizedplanning algorithm where agents can communicate despite notbeing in LOS of each other.

@inproceedings{tuck2021ccta, title = {{DEC-LOS-RRT:} Decentralized path planning for multi-robot systems with Line-of-sight constrained communication}, author = {Tuck, Victoria and Pant, Yash Vardhan and Seshia, Sanjit A. and Sastry, S Shankar}, booktitle = {2021 IEEE Conference on Control Technology and Applications (CCTA)}, year = {2021}, organization = {IEEE}, img = {ccta21.png} }
FADS: A Framework for Autonomous Drone Safety Using Temporal Logic-Based Trajectory Planning

Pant, Yash Vardhan, Li, Max Z., Rodionova, Alena, Quaye, Rhudii A., Abbas, Houssam, Ryerson, Megan, and Mangharam, Rahul

In Transportation Research, Part C: Emerging Technologies, special issue on Embracing Urban Air Mobility 2021

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@inproceedings{pant2019fads, title = {{FADS: A Framework for Autonomous Drone Safety Using Temporal Logic-Based Trajectory Planning}}, author = {Pant, Yash Vardhan and Li, Max Z. and Rodionova, Alena and Quaye, Rhudii A. and Abbas, Houssam and Ryerson, Megan and Mangharam, Rahul}, booktitle = {Transportation Research, Part C: Emerging Technologies, special issue on Embracing Urban Air Mobility}, year = {2021}, img = {trc.png} }
Co-design of Control and Planning for Multi-rotor UAVs with Signal Temporal Logic Specifications

Pant, Yash Vardhan, Yin, He, Arcak, Murat, and Seshia, Sanjit A.

In American Control Conference (ACC) 2021

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Urban Air Mobility (UAM), or the scenario where multiple manned and Unmanned Aerial Vehicles (UAVs) carry out various tasks over urban airspaces, is a transportation concept of the future that is gaining prominence. UAM missions with complex spatial, temporal and reactive requirements can be succinctly represented using Signal Temporal Logic (STL), a behavioral specification language. However, planning and control of systems with STL specifications is computationally intensive, usually resulting in planning approaches that do not guarantee dynamical feasibility, or control approaches that cannot handle complex STL specifications. Here, we present an approach to co-design the planner and control such that a given STL specification (possibly over multiple UAVs) is satisfied with trajectories that are dynamically feasible and our controller can track them with a bounded tracking-error that the planner accounts for. The tracking controller is formulated for the non-linear dynamics of the individual UAVs, and the tracking error bound is computed for this controller when the trajectories satisfy some kinematic constraints. We also augment an existing multi-UAV STL-based trajectory generator in order to generate trajectories that satisfy such constraints. We show that this co-design allows for trajectories that satisfy a given STL specification, and are also dynamically feasible in the sense that they can be tracked with bounded error. The applicability of this approach is demonstrated through simulations of multi-UAV missions.

@inproceedings{pant2020co, title = {Co-design of Control and Planning for Multi-rotor UAVs with Signal Temporal Logic Specifications}, author = {Pant, Yash Vardhan and Yin, He and Arcak, Murat and Seshia, Sanjit A.}, booktitle = {American Control Conference (ACC)}, year = {2021}, pdf = {https://arxiv.org/pdf/2009.14363.pdf}, img = {galaxy2.png} }
Counterexample-Guided Synthesis of Perception Models and Control

Ghosh, Shromona, Pant, Yash Vardhan, Ravanbakhsh, Hadi, and Seshia, Sanjit A.

In American Control Conference (ACC) 2021

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Recent advances in learning-based perception systems have led to drastic improvements in the performance of robotic systems like autonomous vehicles and surgical robots. These perception systems, however, are hard to analyze and errors in them can propagate to cause catastrophic failures. In this paper, we consider the problem of synthesizing safe and robust controllers for robotic systems which rely on complex perception modules for feedback. We propose a counterexample-guided synthesis framework that iteratively builds simple surrogate models of the complex perception module and enables us to find safe control policies. The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it. These models are then used to synthesize controllers that are robust to errors in perception. If the resulting policy is not safe, we gather new counterexamples. By repeating the process, we eventually find a controller which can keep the system safe even when there is a perception failure. We demonstrate our framework on two scenarios in simulation, namely lane keeping and automatic braking, and show that it generates controllers that are safe, as well as a simpler model of a deep neural network-based perception system that can provide meaningful insight into operations of the perception system.

@inproceedings{ghosh2021ceg, title = {{Counterexample-Guided Synthesis of Perception Models and Control}}, author = {Ghosh, Shromona and Pant, Yash Vardhan and Ravanbakhsh, Hadi and Seshia, Sanjit A.}, booktitle = {American Control Conference (ACC)}, year = {2021}, img = {CEG.png} }
Learning-’N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme

Rodionova, Alëna, Pant, Yash Vardhan, Kurtz, Connor, Jang, Kuk, Abbas, Houssam, and Mangharam, Rahul

ACM Transactions on Cyber-Physical Systems 2021

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Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-’N-Flying (LNF) a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UAS as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based decision-making, and b) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UAS on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds), and under certain assumptions has failure rates of less than 1% in the worst-case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.

@article{rodionova2021learning, title = {Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme}, author = {Rodionova, Al{\"e}na and Pant, Yash Vardhan and Kurtz, Connor and Jang, Kuk and Abbas, Houssam and Mangharam, Rahul}, journal = {ACM Transactions on Cyber-Physical Systems}, year = {2021}, pdf = {https://arxiv.org/abs/2101.10404}, selected = {true}, img = {TCPS21.png} }

2020

Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility

Rodionova, Alëna*, Pant, Yash Vardhan*, Jang, Kuk J., Abbas, Houssam, and Mangharam, Rahul

In IEEE International Conference on Intelligent Transportation Systems 2020

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With increasing urban population, there is global interest in Urban Air Mobility (UAM), where hundreds of autonomous Unmanned Aircraft Systems (UAS) execute missions in the airspace above cities. Unlike traditional human-in-the-loop air traffic management, UAM requires decentralized autonomous approaches that scale for an order of magnitude higher aircraft densities and are applicable to urban settings. We present Learning-to-Fly (L2F), a decentralized on-demand airborne collision avoidance framework for multiple UAS that allows them to independently plan and safely execute missions with spatial, temporal and reactive objectives expressed using Signal Temporal Logic. We formulate the problem of predictively avoiding collisions between two UAS without violating mission objectives as a Mixed Integer Linear Program (MILP). This however is intractable to solve online. Instead we develop L2F, a two-stage collision avoidance method that consists of: 1)a learning-based decision making scheme and 2) a distributed, linear programming-based UAS control algorithm. Through extensive simulations, we show the real-time applicability of our method which is ≈ 6000× faster than the MILP approach and can resolve 99.4% of collisions when there is ample room to maneuver, and shows graceful degradation in performance otherwise. We also compare L2F to two other methods and demonstrate an implementation on quad-rotor robots.

@inproceedings{alena2020l2f, title = {{Learning-to-Fly: Learning-based Collision Avoidance for Scalable Urban Air Mobility}}, author = {Rodionova, Alëna* and Pant, Yash Vardhan* and Jang, Kuk J. and Abbas, Houssam and Mangharam, Rahul}, booktitle = {IEEE International Conference on Intelligent Transportation Systems}, year = {2020}, pdf = {https://arxiv.org/abs/2006.13267}, img = {l2f.png} }
Learning-to-Fly RL: Reinforcement Learning-based Collision Avoidance for Scalable Urban Air Mobility

Jang, Kuk J., Pant, Yash Vardhan, Rodionova, Alena, and Mangharam, Rahul

In AIAA/IEEE Digital Avionics Systems Conference (DASC) 2020

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As hundreds of Unmanned Aircraft System (UAS) operate within urban airspaces, automated and decentralized UAS traffic management (UTM) will be critical to maintain safe and efficient operations. In this work, we present Learning-to-Fly with Reinforcement Learning (L2F-RL), a decentralized, on-demand Collision Avoidance (CA) framework that systematically combines machine learning with cooperative model predictive control for UAS collision avoidance while retaining satisfaction of higher-level mission objectives. L2F-RL consists of: 1) RL-based policy for conflict resolution (CR) with discrete-decision making, 2) decentralized, cooperative model predictive control for CA. To accelerate training with RL, we utilize reward shaping and curriculum learning. Our approach outperforms baseline approaches with a 99.01% separation rate (ratio of success to total test cases) in the worst case, improving to 100% in the best case with a 1000X improvement in computation time compared to centralized methods. Our results demonstrate the potential of combining learning approaches with optimization-based control, making it a significant contribution towardsscalable, decentralized UTM.

@inproceedings{jang2020dasc, title = {{Learning-to-Fly RL: Reinforcement Learning-based Collision Avoidance for Scalable Urban Air Mobility}}, author = {Jang, Kuk J. and Pant, Yash Vardhan and Rodionova, Alena and Mangharam, Rahul}, booktitle = {AIAA/IEEE Digital Avionics Systems Conference (DASC)}, year = {2020}, img = {l2f_kj1.png} }
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World

Fremont, Daniel J, Kim, Edward, Pant, Yash Vardhan, Seshia, Sanjit A, Acharya, Atul, Bruso, Xantha, Wells, Paul, Lemke, Steve, Yu, Qiang, and Mehta, Shalin

In IEEE International Conference on Intelligent Transportation Systems (to appear) 2020

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We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world. Our approach is based on formal methods, combining formal specification of scenarios and safety properties, algorithmic test case generation using formal simulation, test case selection for track testing, executing test cases on the track, and analyzing the resulting data. Experiments with a real autonomous vehicle at an industrial testing ground support our hypotheses that (i) formal simulation can be effective at identifying test cases to run on the track, and (ii) the gap between simulated and real worlds can be systematically evaluated and bridged.

@inproceedings{fremont2020formal, title = {{Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World}}, author = {Fremont, Daniel J and Kim, Edward and Pant, Yash Vardhan and Seshia, Sanjit A and Acharya, Atul and Bruso, Xantha and Wells, Paul and Lemke, Steve and Yu, Qiang and Mehta, Shalin}, booktitle = {IEEE International Conference on Intelligent Transportation Systems (to appear)}, year = {2020}, pdf = {https://arxiv.org/pdf/2003.07739.pdf}, img = {itsc20.png} }
Anytime Computation and Control for Autonomous Systems

Pant, Yash Vardhan, Abbas, Houssam, Mohta, Kartik, Quaye, Rhudii A., Nghiem, Truong X., Devietti, Joseph, and Mangharam, Rahul

IEEE Transactions on Control Systems Technology 2020

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The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. Crucial to the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators often use computationally expensive perception algorithms like visual feature tracking. With on-board computers on autonomous robots being computationally limited, the computation time of such an estimation algorithm can at times be high enough to result in poor control performance. We develop a framework for codesign of anytime estimation and robust control algorithms, taking into account computation delays and estimation inaccuracies. This is achieved by constructing an anytime estimator from an off-the-shelf perception-based estimation algorithm and obtaining a trade-off curve for its computation time versus estimation error. This is used in the design of a robust predictive control algorithm that at run-time decides a contract, or operation mode, for the estimator in addition to controlling the dynamical system to meet its control objectives at a reduced computation energy cost. This codesign provides a mechanism through which the controller can use the tradeoff curve to reduce estimation delay at the cost of higher inaccuracy, while guaranteeing satisfaction of control objectives. Experiments on a hexrotor platform running a visual-based algorithm for state estimation show how our method results in up to a 10% improvement in control performance while simultaneously saving 5%-6% in computation energy as compared to a method that does not leverage the codesign.

@article{pant2020tcst, title = {{Anytime Computation and Control for Autonomous Systems}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mohta, Kartik and Quaye, Rhudii A. and Nghiem, Truong X. and Devietti, Joseph and Mangharam, Rahul}, journal = {IEEE Transactions on Control Systems Technology}, year = {2020}, pdf = {TCST20.pdf}, selected = {true}, img = {TCST20.png} }

2019

Robustness Evaluation of Computer-aided Clinical trials for Medical Devices

Jang, Kuk Jin, Pant, Yash Vardhan, Zhang, Bo, Weimer, James, and Mangharam, Rahul

In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2019

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Medical cyber-physical systems, such as the implantable cardioverter defibrillator (ICD), require evaluation of safety and efficacy in the context of a patient population in a clinical trial. Advances in computer modeling and simulation allow for generation of a simulated cohort or virtual cohort which mimics a patient population and can be used as a source of prior information. A major obstacle to acceptance of simulation results as a source of prior information is the lack of a framework for explicitly modeling sources of uncertainty in simulation results and quantifying the effect on trial outcomes. In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation results at all stages of a clinical trial. To quantify the robustness of the CACT outcome with respect to a simulation assumption, we define δ-robustness as the minimum perturbation of the base prior distribution resulting in a change of the CACT outcome and provide a method to estimate the δ-robustness. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial through an application to the Rhythm ID Goes Head-to-head Trial (RIGHT), which was a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order to validate and confirm the results of a CACT for implantable cardiac devices.

@inproceedings{jang2019iccps, title = {{Robustness Evaluation of Computer-aided Clinical trials for Medical Devices}}, author = {Jang, Kuk Jin and Pant, Yash Vardhan and Zhang, Bo and Weimer, James and Mangharam, Rahul}, booktitle = {Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)}, year = {2019}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1137&context=mlab_papers}, img = {cact.png} }
Temporal Logic Robustness for General Signal Classes

Abbas, Houssam, Pant, Yash Vardhan, and Mangharam, Rahul

In Proceedings of the ACM International Conference on Hybrid Systems: Computation and Control (HSCC) 2019

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In multi-agent systems, robots transmit their planned trajectories to each other or to a central controller, and each receiver plans its own actions by maximizing a measure of mission satisfaction. For missions expressed in temporal logic, the robustness function plays the role of satisfaction measure. Currently, a Piece-Wise Linear (PWL) or piece-wise constant reconstruction is used at the receiver. This allows an efficient robustness computation algorithm - a.k.a. monitoring - but is not adaptive to the signal class of interest, and does not leverage the compression properties of more general representations. When communication capacity is at a premium, this is a serious bottleneck. In this paper we first show that the robustness computation is significantly affected by how the continuous-time signal is reconstructed from the received samples, which can mean the difference between a successful control and a crash. We show that monitoring general spline-based reconstructions yields a smaller robustness error, and that it can be done with the same time complexity as monitoring the simpler PWL reconstructions. Thus robustness computation can now be adapted to the signal class of interest. We further show that the monitoring error is tightly upper-bounded by the L ∞ signal reconstruction error. We present a (non-linear) L ∞ -based scheme which yields even lower monitoring error than the spline-based schemes (which have the advantage of being faster to compute), and illustrate all results on two case studies. As an application of these results, we show how time-frequency specifications can be efficiently monitored online.

@inproceedings{abbas2019hscc, title = {{Temporal Logic Robustness for General Signal Classes}}, author = {Abbas, Houssam and Pant, Yash Vardhan and Mangharam, Rahul}, booktitle = {{Proceedings of the ACM International Conference on Hybrid Systems: Computation and Control (HSCC)}}, year = {2019}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1138&context=mlab_papers}, img = {hscc19.png} }
Fly-by-Logic: A Tool for Unmanned Aircraft System Fleet Planning using Temporal Logic

Pant, Yash Vardhan, Quaye, Rhudii A., Abbas, Houssam, Varre, Akarsh, and Mangharam, Rahul

In NASA Formal Methods Symposium 2019

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Safe planning for fleets of Unmaned Aircraft Systems (UAS) performing complex missions in urban environments has typically been a challenging problem. In the United States of America, the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) have been studying the regulation of the airspace when multiple such fleets of autonomous UAS share the same airspace, outlined in the Concept of Operations document (ConOps). While the focus is on the infrastructure and management of the airspace, the Unmanned Aircraft System (UAS) Traffic Management (UTM) ConOps also outline a potential airspace reservation based system for operation where operators reserve a volume of the airspace for a given time inter- val to operate in, but it makes clear that the safety (separation from other aircraft, terrain, and other hazards) is a responsibility of the drone fleet operators. In this work, we present a tool that allows an operator to plan out missions for fleets of multi-rotor UAS, performing complex time- bound missions. The tool builds upon a correct-by-construction planning method by translating missions to Signal Temporal Logic (STL). Along with a simple user interface, it also has fast and scalable mission planning abilities. We demonstrate our tool for one such mission.

@inproceedings{pant2019tool, title = {{Fly-by-Logic: A Tool for Unmanned Aircraft System Fleet Planning using Temporal Logic}}, author = {Pant, Yash Vardhan and Quaye, Rhudii A. and Abbas, Houssam and Varre, Akarsh and Mangharam, Rahul}, booktitle = {NASA Formal Methods Symposium}, year = {2019}, organization = {NASA}, code = {https://github.com/yashpant/FlyByLogic}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1139&context=mlab_papers}, img = {nasafm.png} }

2018

Design and implementation of a centralized system for autonomous unmanned aerial vehicle trajectory conflictresolution

Li, Max Z., Tan, William R., Prakash, Sahithya M., Kennedy, John F., Ryerson, Megan S., Lee, Daewon, and Pant, Yash Vardhan

In IEEE National Aerospace and Electronics Conference (NAECON) 2018

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The ability of autonomous unmanned aerial vehicles(aUAVs) to carry out a diverse range of missions presentsenormous societal and economic benefits. However, one criticalsafety issue that must be addressed prior to large-scale aUAVadoption is the potential for aUAV-on-aUAV trajectory conflicts.Current decentralized methods for trajectory conflict resolutionare energy and resource intensive, rendering them difficult toscale and implement. We propose a centralized, infrastructure-to-vehicle (I2V) approach that leverages modular embedded systems(“control modules”). A voxel-based airspace reservation systemis maintained by the control module, which interrogates andretrieves the stored trajectories from aUAVs approaching thereservation airspace. Trajectory conflict search is performed by the control module, and conflict resolution protocols aretransmitted back to the aUAVs. Our communication protocolis Wi-Fi-based and leverages a lightweight on-board ODROIDmicrocontroller; the setup is agnostic to the specific aUAVplatform and firmware. We simulate various airspace situationsand test the resiliency of our reservation system in MATLAB. Wealso leverage two aUAV testbeds to demonstrate the performanceof our centralized air traffic control regime.

@inproceedings{li2018naecon, title = {{Design and implementation of a centralized system for autonomous unmanned aerial vehicle trajectory conflictresolution}}, author = {Li, Max Z. and Tan, William R. and Prakash, Sahithya M. and Kennedy, John F. and Ryerson, Megan S. and Lee, Daewon and Pant, Yash Vardhan}, booktitle = {IEEE National Aerospace and Electronics Conference (NAECON)}, year = {2018}, pdf = {https://ieeexplore.ieee.org/document/8556719}, img = {max18.png} }
Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives

Pant, Yash Vardhan, Abbas, Houssam, Quaye, Rhudii A, and Mangharam, Rahul

In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2018

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The problem of safe planning and control for multi- drone systems across a variety of missions is of critical impor- tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL spec- ification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method.

@inproceedings{pant2018fly, title = {{Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Quaye, Rhudii A and Mangharam, Rahul}, booktitle = {Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)}, pages = {186--197}, year = {2018}, organization = {IEEE Press}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1127&context=mlab_papers}, code = {https://github.com/yashpant/FlyByLogic}, selected = {true}, img = {FbL_iccps.png} }

2017

Smooth Operator: Control using the Smooth Robustness of Temporal Logic

Pant, Yash Vardhan*, Abbas, Houssam*, and Mangharam, Rahul

In IEEE Conference on Control Technology and Applications (CCTA) 2017

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Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outperforms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems.

@inproceedings{pant2017smooth, title = {{Smooth Operator: Control using the Smooth Robustness of Temporal Logic}}, author = {Pant, Yash Vardhan* and Abbas, Houssam* and Mangharam, Rahul}, booktitle = {IEEE Conference on Control Technology and Applications (CCTA)}, pages = {1235--1240}, year = {2017}, organization = {IEEE}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1119&context=mlab_papers}, code = {https://github.com/yashpant/SmoothOperator0}, img = {sop.png} }

2016

Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints via Feedback Linearization

Pant, Yash Vardhan, Abbas, Houssam, and Mangharam, Rahul

In IEEE Conference on Decision and Control (CDC) 2016

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Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance.

@inproceedings{pant2016robust, title = {{Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints via Feedback Linearization}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, booktitle = {IEEE Conference on Decision and Control (CDC)}, pages = {5694--5699}, year = {2016}, organization = {IEEE}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1113&context=mlab_papers}, img = {cdc16.png} }

2015

Co-design of Anytime Computation and Robust Control

Pant, Yash Vardhan, Abbas, Houssam, Mohta, Kartik, Nghiem, Truong X., Devietti, Joseph, and Mangharam, Rahul

In IEEE Real-Time Systems Symposium (RTSS) 2015

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Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performanceof a control system is the execution time and accuracy ofthe state estimator that provides the controller with stateinformation. This estimator is typically perception-based (e.g.,Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platformbecome overloaded, the estimation delay can compromise con-trol performance and even stability. In this paper, we define a framework for co-designing anytime estimation and controlalgorithms, in a manner that accounts for implementation issues like delays and inaccuracies. We construct an anytimeperception-based estimator from standard off-the-shelf Com-puter Vision algorithms, and show how to obtain a trade-offcurve for its delay vs estimate error behavior. We use this anytime estimator in a controller that can use this trade-off curve at runtime to achieve its control objectives at areduced energy cost. When the estimation delay is too largefor correct operation, we provide an optimal manner in which the controller can use this curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on anautonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.

@inproceedings{pant2015co, title = {{Co-design of Anytime Computation and Robust Control}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mohta, Kartik and Nghiem, Truong X. and Devietti, Joseph and Mangharam, Rahul}, booktitle = {IEEE Real-Time Systems Symposium (RTSS)}, pages = {43--52}, year = {2015}, organization = {IEEE}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1110&context=mlab_papers}, img = {rtss15_2.png} }
Power-efficient Algorithms for Autonomous Navigation

Pant, Yash Vardhan, Abbas, Houssam, Nischal, KN, Kelkar, Paritosh, Kumar, Dhruva, Devietti, Joseph, and Mangharam, Rahul

In International Conference on Complex Systems Engineering (ICCSE) 2015

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Real-time navigation of autonomous vehicles requires the processing of a large amount of sensor data by the perception algorithms onboard the vehicle, like object detection and localization. To meet the driving performance and safety requirements, these algorithms require the hardware to be over-engineered to always operate for the worst-case. This leads to excessive power consumption by the computation platform. In this paper, we study how platform-level optimizations affect the computation throughput and power, and how to use this trade-off to save computation power without overly degrading throughput and control performance. The approach uses an offline profiling stage of the perception algorithm, which gives us Throughput versus Power curves for various processor frequencies and various scheduling of the perception code on CPU and GPU. At runtime, we combine power and throughput into one objective function, and design a supervisor what will determine the frequency and CPU/GPU allocation to maximize the objective. We illustrate our approach on a scaled-down autonomous car which uses Vanishing Point navigation. Experimental results demonstrate that we can achieve an energy savings of upto 20% while degrading control performance by less than 1%.

@inproceedings{pant2015power, title = {{Power-efficient Algorithms for Autonomous Navigation}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Nischal, KN and Kelkar, Paritosh and Kumar, Dhruva and Devietti, Joseph and Mangharam, Rahul}, booktitle = {International Conference on Complex Systems Engineering (ICCSE)}, pages = {1--6}, year = {2015}, organization = {IEEE}, pdf = {ICCSE15.pdf}, img = {iccse15.png} }

2014

Peak Power Reduction in Hybrid Energy Systems with Limited Load Forecasts

Pant, Yash Vardhan, Nghiem, Truong X, and Mangharam, Rahul

In American Control Conference (ACC) 2014

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Hybrid energy systems, which consist of a load powered by a source and a form of energy storage, find applications in many systems, e.g., the electric grid and electric vehicles. A key problem for hybrid energy systems is the reduction of peak power consumption to ensure cost-efficient operation as peak power draws require additional resources and adversely affect the system reliability and lifetime. Furthermore, in some cases such as electric vehicles, the load dynamics are fast, not perfectly known in advance and the on-board computation power is often limited, making the implementation of traditional optimal control difficult. We aim to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts. We propose a scheme with two control levels and provide a sufficient condition for control of the different energy storage/generation components to meet the instantaneous load while satisfying a peak power threshold. The scheme provides performance comparable to Model Predictive Control, while requiring less computation power and only coarse-grained load predictions. For a case study, we implement the scheme for a battery-supercapacitor-powered electric vehicle with real world drive cycles to demonstrate the low execution time and effective reduction of the battery power (hence temperature), which is crucial to the lifetime of the battery.

@inproceedings{pant2014peak, title = {{Peak Power Reduction in Hybrid Energy Systems with Limited Load Forecasts}}, author = {Pant, Yash Vardhan and Nghiem, Truong X and Mangharam, Rahul}, booktitle = {American Control Conference (ACC)}, pages = {4212--4217}, year = {2014}, organization = {IEEE}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1084&context=mlab_papers}, img = {acc14.png} }

2011

Autoplug: An automotive test-bed for electronic controller unit testing and verification

Drolia, Utsav*, Wang, Zhenyan*, Pant, Yash Vardhan*, and Mangharam, Rahul

In IEEE International Conference on Intelligent Transportation Systems 2011

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In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, update and verify controls software. AutoPlug consists of multiple ECUs interconnected by a CAN bus, a race car driving simulator which behaves as the plant model and a vehicle controls monitor in Matlab. As the ECUs drive the simulated vehicle, the physicsbased simulation provides feedback to the controllers in terms of acceleration, yaw, friction and vehicle stability. This closedloop platform is then used to evaluate multiple vehicle control software modules such as traction, stability and cruise control. With this test-bed we highlight approaches for runtime ECU software diagnosis and testing of the stability and performance of the vehicle. Code updates can be executed via a smart phone so drivers may remotely “patch” their vehicle. This closedloop automotive control test-bed allows the automotive research community to explore the capabilities and challenges of safe and secure remote code updates for vehicle recalls management.

@inproceedings{drolia2011autoplug, title = {{Autoplug: An automotive test-bed for electronic controller unit testing and verification}}, author = {Drolia, Utsav* and Wang, Zhenyan* and Pant, Yash Vardhan* and Mangharam, Rahul}, booktitle = {IEEE International Conference on Intelligent Transportation Systems}, pages = {1187--1192}, year = {2011}, organization = {IEEE}, pdf = {https://repository.upenn.edu/cgi/viewcontent.cgi?article=1045&context=mlab_papers}, img = {itsc11.png} }

Submitted and in preparation

2021

Distributed Trajectory Planning for Multi-rotor UAV fleets with Signal Temporal Logic Objectives

Pant, Yash Vardhan, Abbas, Houssam, and Mangharam, Rahul

In In preparation 2021

Bib

@inproceedings{pant2021dec, title = {{Distributed Trajectory Planning for Multi-rotor UAV fleets with Signal Temporal Logic Objectives}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, booktitle = {In preparation}, year = {2021}, img = {pika.jpg} }

Demo abstracts, Workshop papers and Works-in-Progress

2019

Distributed planning of Multi-rotor drone fleets using the Smooth Robustness of Signal Temporal Logic

Pant, Yash Vardhan, Abbas, Houssam, and Mangharam, Rahul

In Monitoring and Testing of CPS Workshop (MTCPS) at CPS Week 2019

Bib

@inproceedings{pantMTCPS19, title = {{Distributed planning of Multi-rotor drone fleets using the Smooth Robustness of Signal Temporal Logic}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, year = {2019}, booktitle = {{Monitoring and Testing of CPS Workshop (MTCPS) at CPS Week}}, img = {pika.jpg} }

2017

Control with Temporal Logic Requirements (poster)

Pant, Yash Vardhan, Abbas, Houssam, and Mangharam, Rahul

In SRC TECHCON 2017

Bib

@inproceedings{pantSRC17, title = {{Control with Temporal Logic Requirements (poster)}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, year = {2017}, booktitle = {{SRC TECHCON}}, note = {tech}, img = {pika.jpg} }
Control using the Smooth Robustness of Temporal Logic

Pant, Yash Vardhan, Abbas, Houssam, and Mangharam, Rahul

In Monitoring and Testing of CPS Workshop (MTCPS) at CPS Week 2017

Bib

@inproceedings{pantMTCPS17, title = {{Control using the Smooth Robustness of Temporal Logic}}, author = {Pant, Yash Vardhan and Abbas, Houssam and Mangharam, Rahul}, year = {2017}, booktitle = {{Monitoring and Testing of CPS Workshop (MTCPS) at CPS Week}}, img = {pika.jpg} }

2015

Hardware Optimizations for Anytime Perception and Control

Nischal, K. N., Kelkar, Paritosh, Kumar, Dhruva, Pant, Yash Vardhan, Abbas, Houssam, Devietti, Joseph, and Mangharam, Rahul

In Work-in-progress, Real-Time Systems Symposium (RTSS) 2015

Bib

@inproceedings{KNRTSS15, title = {{Hardware Optimizations for Anytime Perception and Control}}, author = {Nischal, K. N. and Kelkar, Paritosh and Kumar, Dhruva and Pant, Yash Vardhan and Abbas, Houssam and Devietti, Joseph and Mangharam, Rahul}, year = {2015}, booktitle = {{Work-in-progress, Real-Time Systems Symposium (RTSS)}}, img = {pika.jpg} }

2014

Demo: Low-cost Autonomous Navigation with Anytime Control and Computation

Gurniak, Paul, and Pant, Yash Vardhan

In University Transportation Center (UPenn-CMU) Annual Meeting 2014

Bib

@inproceedings{Gurniak14, title = {{Demo: Low-cost Autonomous Navigation with Anytime Control and Computation}}, author = {Gurniak, Paul and Pant, Yash Vardhan}, year = {2014}, booktitle = {{University Transportation Center (UPenn-CMU) Annual Meeting}}, img = {pika.jpg} }

2013

Knock NOx: Model-based Remote Diagnostics of a Diesel Exhaust Control System

Pant, Yash Vardhan, Nghiem, Truong X, and Mangharam, Rahul

In Work-in-Progress IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) 2013

Bib

@inproceedings{pant2013knock, title = {Knock NOx: Model-based Remote Diagnostics of a Diesel Exhaust Control System}, author = {Pant, Yash Vardhan and Nghiem, Truong X and Mangharam, Rahul}, booktitle = {Work-in-Progress IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)}, pages = {17--20}, year = {2013}, img = {pika.jpg} }
Final Report: Protodrive: Rapid Prototyping and Simulation of Electric Vehicle Powertrains

Pant, Yash Vardhan, Jain, Harsh, Mulay, Abhijeet, and Dutta, Rajib

In Intel Cornell Cup 2013

Bib

@inproceedings{CornellCup13, title = {{Final Report: Protodrive: Rapid Prototyping and Simulation of Electric Vehicle Powertrains}}, author = {Pant, Yash Vardhan and Jain, Harsh and Mulay, Abhijeet and Dutta, Rajib}, year = {2013}, booktitle = {{Intel Cornell Cup}}, img = {pika.jpg} }
Final Report: Protodrive: Simulation of Electric Vehicle Powertrains

Price, William, Jain, Harsh, Pant, Yash Vardhan, and Mangharam, Rahul

In World Embedded Software Competition 2013

Bib

@inproceedings{WESC13, title = {{Final Report: Protodrive: Simulation of Electric Vehicle Powertrains}}, author = {Price, William and Jain, Harsh and Pant, Yash Vardhan and Mangharam, Rahul}, year = {2013}, booktitle = {{World Embedded Software Competition}}, img = {pika.jpg} }

2012

Protodrive: An experimental platform for electric vehicle energy scheduling and control

Diaz, Stephanie, Jain, Harsh, Pant, Yash Vardhan, and Mangharam, Rahul

In Demo Abstract Real-Time Systems Symposium (RTSS) 2012

Bib

@inproceedings{Diaz12, title = {{Protodrive: An experimental platform for electric vehicle energy scheduling and control}}, author = {Diaz, Stephanie and Jain, Harsh and Pant, Yash Vardhan and Mangharam, Rahul}, year = {2012}, booktitle = {{Demo Abstract Real-Time Systems Symposium (RTSS)}}, img = {pika.jpg} }
AutoPlug 2.0

Pant, Yash Vardhan

In Real-Time and Embedded Technology and Applications Symposium (RTAS): Demo session at CPS Week 2012

Bib

@inproceedings{plug12, title = {{AutoPlug 2.0}}, author = {Pant, Yash Vardhan}, year = {2012}, booktitle = {{Real-Time and Embedded Technology and Applications Symposium (RTAS): Demo session at CPS Week}}, img = {pika.jpg} }
Observer-based Sensor Fault Detection and Isolation

Pant, Yash Vardhan, and Mangharam, Rahul

In Work-in-progress, ACM International Conference on High Confidence Networked Systems (HiCoNS) at CPS Week 2012

Bib

@inproceedings{hicons12, title = {{Observer-based Sensor Fault Detection and Isolation}}, author = {Pant, Yash Vardhan and Mangharam, Rahul}, year = {2012}, booktitle = {{Work-in-progress, ACM International Conference on High Confidence Networked Systems (HiCoNS) at CPS Week}}, img = {pika.jpg} }