Conformalized Reachable Sets for Obstacle Avoidance With Spheres

TL;DR

CROWS is a real-time, probabilistically-safe motion planning method for robots that uses neural networks and conformal prediction to generate collision-free trajectories in cluttered environments.

Contribution

It introduces a novel neural network-based reachable set representation combined with conformal prediction for probabilistic safety guarantees in real-time motion planning.

Findings

CROWS outperforms state-of-the-art methods in cluttered environments.

It provides probabilistic safety guarantees for robot trajectories.

The method is demonstrated with real-time experiments and code availability.

Abstract

Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments. Motion plans must be safe to ensure that the robot does not harm humans or damage any nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environment. Many trajectory optimization methods introduce heuristics that balance safety and real-time performance, potentially increasing the risk of the robot colliding with its environment. This paper addresses this challenge by proposing Conformalized Reachable Sets for Obstacle Avoidance With Spheres (CROWS). CROWS is a novel real-time, receding-horizon trajectory planner that generates probalistically-safe motion plans. Offline, CROWS learns a novel neural network-based representation of a spherebased reachable set that overapproximates the swept volume of the robot's motion. CROWS then uses conformal prediction to compute a confidence bound that provides a probabilistic safety guarantee on the learned reachable set. At runtime, CROWS performs trajectory optimization to select a trajectory that is probabilstically-guaranteed to be collision-free. We demonstrate that CROWS outperforms a variety of state-of-the-art methods in solving challenging motion planning tasks in cluttered environments while remaining collision-free. Code, data, and video demonstrations can be found at https://roahmlab.github.io/crows/