Paper presented at the 16th European-African Regional Conference of the ISTVS
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Title: Trajectory optimization for vegetation override in off-road driving
Authors: Charles Noren, Bhaskar Vundurthy, Sebastian Scherer, and Matthew Travers
Abstract: Off-road robotic vehicles often encounter environmental objects while traversing complex terrains. These objects may obstruct the vehicle’s motion and must be perceived and reasoned about to ensure safe operation. Historically, limited sensing fidelity for autonomous vehicles led to either overly cautious or unnecessarily risky behaviors when interacting with environmental objects. Some objects must be avoided to ensure operational integrity, but avoiding all environmental objects will likely cause needlessly slow traversals. With contemporary sensing, the capability to tell the difference between these environmental objects is now possible. Given this improved sensing capability, developing controls algorithms which not only reason about avoiding certain classes of objects but can also reason about motions through other classes of objects is now the next step towards robust off-road autonomous driving. In this work, we propose a trajectory optimization technique for overriding vegetation that explicitly encodes contact constraints for a subset of vegetative objects found in the operating environment. The subset of objects that the vehicle can safely collide with is derived from existing parameterized collision models, the parameters of which are estimated by the on-board sensing system, and then characterized by a necessary velocity at the point of collision. These constraints are then enforced during vehicle run-time by the controller. The controller's capabilities are shown in both simulation and used to control a full-sized autonomous utility task vehicle. The vehicle’s determination to avoid or override objects is robust to uncertainty in the sensing system and is dependent on the specifics of the environment and object geometry.
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