7,391 research outputs found
Contingency Model Predictive Control for Automated Vehicles
We present Contingency Model Predictive Control (CMPC), a novel and
implementable control framework which tracks a desired path while
simultaneously maintaining a contingency plan -- an alternate trajectory to
avert an identified potential emergency. In this way, CMPC anticipates events
that might take place, instead of reacting when emergencies occur. We
accomplish this by adding an additional prediction horizon in parallel to the
classical receding MPC horizon. The contingency horizon is constrained to
maintain a feasible avoidance solution; as such, CMPC is selectively robust to
this emergency while tracking the desired path as closely as possible. After
defining the framework mathematically, we demonstrate its effectiveness
experimentally by comparing its performance to a state-of-the-art deterministic
MPC. The controllers drive an automated research platform through a left-hand
turn which may be covered by ice. Contingency MPC prepares for the potential
loss of friction by purposefully and intuitively deviating from the prescribed
path to approach the turn more conservatively; this deviation significantly
mitigates the consequence of encountering ice.Comment: American Control Conference, July 2019; 6 page
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant
supervision for training. Safe exploration algorithms ensure the safety of this
data collection process even when only partial knowledge is available. We
present a new approach for optimal motion planning with safe exploration that
integrates chance-constrained stochastic optimal control with dynamics learning
and feedback control. We derive an iterative convex optimization algorithm that
solves an \underline{Info}rmation-cost \underline{S}tochastic
\underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem
(Info-SNOC). The optimization objective encodes both optimal performance and
exploration for learning, and the safety is incorporated as distributionally
robust chance constraints. The dynamics are predicted from a robust regression
model that is learned from data. The Info-SNOC algorithm is used to compute a
sub-optimal pool of safe motion plans that aid in exploration for learning
unknown residual dynamics under safety constraints. A stable feedback
controller is used to execute the motion plan and collect data for model
learning. We prove the safety of rollout from our exploration method and
reduction in uncertainty over epochs, thereby guaranteeing the consistency of
our learning method. We validate the effectiveness of Info-SNOC by designing
and implementing a pool of safe trajectories for a planar robot. We demonstrate
that our approach has higher success rate in ensuring safety when compared to a
deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-
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