Yash Vardhan Pant

selected publications

1. 

   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

   Abs Bib PDF Code

   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} }

2. 

   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

   Abs Bib PDF

   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} }

3. 

   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

   Abs Bib PDF

   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} }