Model-based control techniques for systems such as legged robots and unmanned aerial vehicles have the ability to explicitly reason about the nonlinearity and uncertainty in the robots' dynamics and potentially even provide guarantees on their safety. However, a fundamental and outstanding challenge is their limited ability to reason about rich sensory inputs such as depth images or vision. Model-based control techniques often treat the robot's perceptual system as a black box and make unrealistic assumptions about the perceptual system's output. The goal of this project is to address these challenges by leveraging data-driven approaches for learning dynamical models of task-relevant perceptual features extracted from rich sensory inputs and using these models for agile and safe robot navigation.
p-ACE: a probabilistic extension to ACE is a light-weight state estimation algorithm for planetary rovers with kinematically constrained articulated suspension systems. ACE's conservative safety check approach can sometimes lead to over-pessimism: feasible states are often reported as infeasible, thus resulting in frequent false positive detection. p-ACE estimates probability distributions over states instead of deterministic bounds to provide more flexible and less pessimistic worst-case evaluation with probabilistic safety guarantees.
ACE is a light-weight collision detection algorithm for motion planning of planetary rovers with articulated suspension systems. Solving the exact collision detection problem for articulated suspension systems requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation.