Swarm Formation Morphing for Congestion Aware Collision Avoidance

TL;DR

This paper introduces a novel formation morphing method for swarm collision avoidance that optimizes obstacle evasion and formation resumption, reducing energy consumption and mission time compared to traditional shortest path approaches.

Contribution

It presents a new two-phase methodology for optimal swarm formation morphing during obstacle avoidance, combining trajectory optimization and TPS-inspired resumption techniques.

Findings

14.7% lower energy consumption compared to traditional shortest path methods

Effective formation morphing reduces mission time and delays

Demonstrated success in test scenario with obstacle avoidance

Abstract

The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize a thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the traditional method based on the shortest path results in 14.7% higher energy consumption as compared to our proposed approach.