An Energy-Optimal Framework for Assignment and Trajectory Generation in Teams of Autonomous Agents

TL;DR

This paper introduces a decentralized, energy-efficient framework for assigning and generating trajectories for homogeneous autonomous agents to reach multiple goals safely, with proven optimality conditions and validated through simulations.

Contribution

It presents a novel decentralized approach combining goal assignment and trajectory generation with energy minimization and safety guarantees for multi-agent systems.

Findings

Framework guarantees safety through dynamic constraints

Algorithm converges to a unique agent-goal assignment under certain conditions

Validated effectiveness through MATLAB simulations

Abstract

In this paper, we present an approach for solving the problem of moving $N$ homogeneous agents into $M \geq N$ goal locations along energy-minimizing trajectories. We propose a decentralized framework that only requires knowledge of the goal locations and partial observations of the global state by each agent. The framework includes guarantees on safety through dynamic constraints, and a method to impose a dynamic, global priority ordering on the agents. A solution to the goal assignment and trajectory generation problems are derived in the form of a binary program and a nonlinear system of equations. Then, we present the conditions for optimality and characterize the conditions under which our algorithm is guaranteed to converge to a unique assignment of agents to goals. We also solve the fully constrained decentralized trajectory generation problem considering the state, control, and safety constraints. Finally, we validate the efficacy of our approach through a numerical simulation in MATLAB.