Chance-Constrained Motion Planning with Event-Triggered Estimation

TL;DR

This paper presents a novel sampling-based motion planning method that integrates event-triggered estimation to reduce communication costs in autonomous navigation, while ensuring probabilistic safety and goal-reaching constraints.

Contribution

It introduces a new approach combining chance-constrained planning with event-triggered estimation and a novel offline state distribution propagation method.

Findings

Fast computation of optimal plans demonstrated in case studies.

Effective reduction of communication costs through event-triggered strategies.

Successful satisfaction of probabilistic safety and goal constraints.

Abstract

We consider the problem of autonomous navigation using limited information from a remote sensor network. Because the remote sensors are power and bandwidth limited, we use event-triggered (ET) estimation to manage communication costs. We introduce a fast and efficient sampling-based planner which computes motion plans coupled with ET communication strategies that minimize communication costs, while satisfying constraints on the probability of reaching the goal region and the point-wise probability of collision. We derive a novel method for offline propagation of the expected state distribution, and corresponding bounds on this distribution. These bounds are used to evaluate the chance constraints in the algorithm. Case studies establish the validity of our approach, demonstrating fast computation of optimal plans.