Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT

TL;DR

This paper introduces a new collision avoidance algorithm for diverse agents using CTMAT shape representation, improving efficiency and reducing false collisions in heterogeneous multi-agent scenarios.

Contribution

The paper proposes a novel CTMAT shape representation and an efficient linear programming approach for reciprocal collision avoidance among heterogeneous agents.

Findings

Comparable runtime performance to existing methods

Less conservative with fewer false collisions

Effective in road traffic scenario benchmarks

Abstract

We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes. We present a novel CTMAT representation based on medial axis transform to compute a tight fitting bounding shape for each agent. Each CTMAT is represented using tuples, which are composed of circular arcs and line segments. Based on the reciprocal velocity obstacle formulation, we reduce the problem to solving a low-dimensional linear programming between each pair of tuples belonging to adjacent agents. We precompute the Minkowski Sums of tuples to accelerate the runtime performance. Finally, we provide an efficient method to update the orientation of each agent in a local manner. We have implemented the algorithm and highlight its performance on benchmarks corresponding to road traffic scenarios and different vehicles. The overall runtime performance is comparable to prior multi-agent collision avoidance algorithms that use circular or elliptical agents. Our approach is less conservative and results in fewer false collisions.