TY - JOUR
T1 - Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization
AU - Rodríguez-Molina, Alejandro
AU - Herroz-Herrera, Axel
AU - Aldape-Pérez, Mario
AU - Flores-Caballero, Geovanni
AU - Antón-Vargas, Jarvin Alberto
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot’s movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better.
AB - Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot’s movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better.
KW - Bug0
KW - differential drive mobile robot
KW - dynamic path planning
KW - metaheuristics
KW - online optimization
UR - http://www.scopus.com/inward/record.url?scp=85141844336&partnerID=8YFLogxK
U2 - 10.3390/math10213990
DO - 10.3390/math10213990
M3 - Artículo
AN - SCOPUS:85141844336
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 21
M1 - 3990
ER -