Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System
Author:
Ribeiro António Fernando Alcântara1ORCID, Lopes Ana Carolina Coelho1ORCID, Ribeiro Tiago Alcântara2ORCID, Pereira Nino Sancho Sampaio Martins3ORCID, Lopes Gil Teixeira4ORCID, Ribeiro António Fernando Macedo2ORCID
Affiliation:
1. Industrial Electronics Department, University of Minho, 4800-058 Guimarães, Portugal 2. Industrial Electronics Department, ALGORITMI Centre, 4800-058 Guimarães, Portugal 3. Dyson Ltd., 86 Hullavington Airfield, Hullavington, Chippenham SN14 6GU, UK 4. INESC TEC, Business Science Department, University of Maia, 4475-690 Maia, Portugal
Abstract
The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition.
Subject
Artificial Intelligence,Control and Optimization,Mechanical Engineering
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