Multi-agent Time-based Decision-making for the Search and Action Problem

TL;DR

This paper presents a decentralized multi-agent decision-making framework that optimizes search and action tasks under time constraints using probabilistic reasoning, demonstrated through simulations and real-world integration.

Contribution

It introduces a novel time-aware, probabilistic decision-making approach for multi-agent systems tackling search and action problems with time limits.

Findings

Outperforms benchmark strategies in simulations

Effective in search, pick, and place tasks

Validated in Gazebo environment for real-world readiness

Abstract

Many robotic applications, such as search-and-rescue, require multiple agents to search for and perform actions on targets. However, such missions present several challenges, including cooperative exploration, task selection and allocation, time limitations, and computational complexity. To address this, we propose a decentralized multi-agent decision-making framework for the search and action problem with time constraints. The main idea is to treat time as an allocated budget in a setting where each agent action incurs a time cost and yields a certain reward. Our approach leverages probabilistic reasoning to make near-optimal decisions leading to maximized reward. We evaluate our method in the search, pick, and place scenario of the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), by using a probability density map and reward prediction function to assess actions. Extensive simulations show that our algorithm outperforms benchmark strategies, and we demonstrate system integration in a Gazebo-based environment, validating the framework's readiness for field application.