Robust Motion Planning

Robust Motion Planning

We’re applying recent ideas from robotic motion planning to atmospheric entry vehicles. Knowledge of a planet’s atmosphere, winds, and the vehicle’s position and velocity are all imperfect. As future NASA missions seek to land larger payloads with greater precision on Mars and elsewhere, effectively reasoning about these uncertainties will be crucial. To meet this challenge, we’re developing a unified framework for modeling, trajectory design, and control that explicitly deals with uncertainty at every stage in the process to enhance performance and safety. Our approach harnesses new tools from optimization to compute “invariant funnels” - tubes around a nominal vehicle trajectory that bound the effects of uncertainties and disturbances like winds. Using these funnels, we can plan trajectories that are guaranteed to meet safety and accuracy requirements while taking full advantage of vehicle capabilities to enhance performance.

Related Papers

PDF Robust Entry Vehicle Guidance with Sampling-Based Invariant Funnels
Remy Derollez, Simon Le Cleac'h, and Zac Manchester
IEEE Aerospace Conference (AeroConf2021). Big Sky, MT.
PDF Sample-Based Robust Uncertainty Propagation for Entry Vehicles
Remy Derollez, and Zac Manchester
AAS Guidance, Navigation, and Control Conference Breckenridge, CO.
PDF Robust Direct Trajectory Optimization Using Approximate Invariant Funnels
Zac Manchester, and Scott Kuindersma
Autonomous Robots
PDF DIRTREL: Robust Trajectory Optimization with Ellipsoidal Distrubances and LQR Feedback
Zac Manchester, and Scott Kuindersma
Robotics: Science and Systems (RSS). Cambridge, Massachusetts.


Zac Manchester
Assistant Professor
Kevin Tracy
Optimization and Control
Giusy Falcone
Assistant Professor at University of Michigan
Last updated: 2018-08-09