Planning and Navigation in Dynamic, Uncertain Environments
Developing autonomy to be reliable in dynamic, uncertain environments has significant potential for societal impact, with application to flight coordination, satellite navigation, aggressive driving conditions, and even household robotics. Despite improvements in autonomous navigation and an explosion in the availability of robotics platforms, significant limitations remain in both autonomous as well as collaborative navigation. We seek novel planning solutions in highly cluttered, dynamic environments that incorporate the stochastic dynamics of moving obstacles. Efforts to integrate robotic platforms into environments with humans as well as other autonomous agents must address the quality of autonomous solutions, limited integration of human guidance, computational efficiency of solutions for safe, real time navigation, and the effective incorporation of uncertainty in decision making aids. We develop algorithms that are robust to uncertainties rife in actual dynamical systems, that incorporate human guidance and intuition, and that provide rigorous, mathematical guarantees about desired behavior.
Related lab publications
Nick Malone, Hao-Tien Chiang, Kendra Lesser, Meeko Oishi, Lydia Tapia, “Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field,” IEEE Transactions on Robotics, vol. 33, no. 5, October 2017, p. 1124-1138.
Related lab conference proceedings
V. Sivaramakrishnan, J. Pilipovsky, M. Oishi, and P. Tsiotras, “Distribution Steering for Discrete-Time Linear Systems with General Disturbances using Characteristic Functions,” 2022 American Control Conference (ACC), 2022, submitted
S. Priore, A. Vinod, V. Sivaramakrishnan, C. Petersen and M. Oishi, “Stochastic multi-satellite maneuvering with constraints in an elliptical orbit,” 2021 American Control Conference (ACC), 2021, pp. 4261-4268.
Thorpe, A.J. and Oishi, M.M., 2021, “Stochastic Optimal Control via Hilbert Space Embeddings of Distributions,” In the Proceedings of IEEE International Conference on Decision and Control, 2021, to appear.
Abraham Vinod, Baisravan HomChaudhuri, Christoph Hintz, Anup Parikh, Stephen P. Buerger, Meeko Oishi, Gregory Brunson, Shakeeb Ahmad, Rafael Fierro, “Coordinated Threat Intercept via Forward Stochastic Reachability,” In the Proceedings of the American Control Conference, Milwaukee, Wisconsin, June 2018, to appear.
B. HomChaudhuri, A. Vinod, M. Oishi, “Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance,” In the Proceedings of American Control Conference, Seattle, WA, May 2017, p. 4404-4411.
Hao-Tien Lewis Chiang, Baisravan HomChaudhuri, Abraham P Vinod, Meeko Oishi, and Lydia Tapia, “Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions,” In the Proceedings of IEEE International Conference on Robotics and Automation, Singapore, May 2017, pp 3762-3769.
H.-T. Chiang, N. Malone, K. Lesser, M. Oishi, and L. Tapia, “Path-Guided Artificial Potential Fields with Stochastic Reachable Sets for Motion Planning in Highly Dynamic Environments,” In the Proceedings of IEEE Int’l Conference on Robotics and Automation Seattle, WA, May 2015, p. 2347-2354.
H.-T. Chiang, N. Malone, K. Lesser, M. Oishi, and L. Tapia, “Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field,” In the Proceedings of the International Workshop on the Algorithmic Foundations of Robotics (WAFR), Istanbul, Turkey, August 2014, p. 73-89.