TY - GEN
T1 - Optimization and Evaluation of a Multi Robot Surface Inspection Task Through Particle Swarm Optimization
AU - Chiu, Darren
AU - Nagpal, Radhika
AU - Haghighat, Bahar
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of 3-cm sized wheeled robots to inspect a randomized black and white tiled surface section of size 1m×1m in simulation. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines the swarm's classification decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to 55% improvement in median of fitness evaluations against an empirically chosen parameter set.
AB - Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of 3-cm sized wheeled robots to inspect a randomized black and white tiled surface section of size 1m×1m in simulation. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines the swarm's classification decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to 55% improvement in median of fitness evaluations against an empirically chosen parameter set.
UR - http://www.scopus.com/inward/record.url?scp=85202439499&partnerID=8YFLogxK
U2 - 10.1109/ICRA57147.2024.10611661
DO - 10.1109/ICRA57147.2024.10611661
M3 - Conference contribution
AN - SCOPUS:85202439499
SN - 979-8-3503-8458-1
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 8996
EP - 9002
BT - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024
PB - IEEE
T2 - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Y2 - 13 May 2024 through 17 May 2024
ER -