Cooperatively Transporting Unknown Objects using Mobile Agents

Abstract

This paper presents an algorithm for cooperatively transporting objects by multiple robots without any initial knowledge. The robots are connected by communication networks, and the controlling algorithm is based on the pheromone communication of social insects such as ants. Unlike traditional pheromone based cooperative transportation, we have implemented the pheromone as mobile software agents that control the mobile robots corresponding to the ants. The pheromone agent has the vector value pointing to its birth location inside, which is used to guide a robot to the birth location. Since the pheromone agent can diffuse with migrations between robots as well as a physical pheromone, it can attract other robots scattering in a work field to the birth location. Once the robot finds an object, it briefly pushes the object, measuring the degree of the inclination of the object. The robot generates a pheromone agent with the vector value to pusing point suitable for suppressing the inclination of the object. The process of the pushes and generations of pheromone agents enables the efficient transportation of the object. We have implemented a simulator based on our algorithm, and conducted experiments to demonstrate the feasibility of our approach.

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

“…Within the field of robotics, MAS have been studied for their applications within Multi-Robot Systems (MRS) (Bayindir, 2016;Schranz et al, 2020;Dias et al, 2021). The use of MRS, which can either be controlled in a centralized or decentralized fashion (Khan et al, 2015), has been demonstrated in a wide variety of tasks, including area mapping (Okumura et al, 2018;Kit et al, 2019), area characterization (Ebert et al, 2018;Ebert et al, 2020), collective construction (Werfel et al, 2014), collective decisionmaking (Valentini et al, 2017), collective transport (Takahashi et al, 2014), perimeter defense or geofencing (Chamanbaz et al, 2017;Shishika and Paley, 2019), as well as target search and tracking (Kamimura and Ohira, 2010;Shah and Schwager, 2019;Kwa et al, 2020a). The attractiveness of MRS in such tasks stem from three key features: 1) flexibility-the ability for the to adapt quickly to rapidly changing environments, 2) robustness-the ability to cope with component failures within the system, and 3) scalability-the ability to carry out tasks in systems comprised of different number of agents (Dorigo et al, 2021).…”