# Distributed Path Planning for Executing Cooperative Tasks with Time Windows

## TL;DR

This paper presents a distributed, game-theoretic approach for multi-robot path planning to maximize task completion with time constraints, adapting to unknown task parameters and periodic task arrivals.

## Contribution

It introduces a novel distributed learning method for multi-robot trajectory planning under uncertain, time-sensitive cooperative tasks with dynamic task arrivals.

## Key findings

- The proposed method improves task completion rates in simulations.
- Robots effectively adapt to unknown task parameters over cycles.
- Distributed learning enhances collective performance in cooperative tasks.

## Abstract

We investigate the distributed planning of robot trajectories for optimal execution of cooperative tasks with time windows. In this setting, each task has a value and is completed if sufficiently many robots are simultaneously present at the necessary location within the specified time window. Tasks keep arriving periodically over cycles. The task specifications (required number of robots, location, time window, and value) are unknown a priori and the robots try to maximize the value of completed tasks by planning their own trajectories for the upcoming cycle based on their past observations in a distributed manner. Considering the recharging and maintenance needs, robots are required to start and end each cycle at their assigned stations located in the environment. We map this problem to a game theoretic formulation and maximize the collective performance through distributed learning. Some simulation results are also provided to demonstrate the performance of the proposed approach.