A Decentralized Control Framework for Energy-Optimal Goal Assignment and Trajectory Generation

TL;DR

This paper introduces a decentralized method for assigning goals and generating energy-efficient, collision-free trajectories for agent swarms, validated through numerical simulations demonstrating robustness and optimality.

Contribution

It presents a novel decentralized assignment algorithm and trajectory generation framework that ensures energy efficiency and collision avoidance in swarm formations.

Findings

The approach guarantees collision-free trajectories.

The method is robust to dynamic goal changes.

Numerical case studies validate energy optimality and performance.

Abstract

This paper proposes a decentralized approach for solving the problem of moving a swarm of agents into a desired formation. We propose a decentralized assignment algorithm which prescribes goals to each agent using only local information. The assignment results are then used to generate energy-optimal trajectories for each agent which have guaranteed collision avoidance through safety constraints. We present the conditions for optimality and discuss the robustness of the solution. The efficacy of the proposed approach is validated through a numerical case study to characterize the framework's performance on a set of dynamic goals.