TY - JOUR
T1 - Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
AU - Montiel, Oscar
AU - Orozco-Rosas, Ulises
AU - Sepúlveda, Roberto
N1 - Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/7/15
Y1 - 2015/7/15
N2 - In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.
AB - In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.
KW - Bacterial Evolutionary Algorithms
KW - Bacterial Potential Field
KW - Collision avoidance
KW - Mobile robots
KW - Path planning
UR - http://www.scopus.com/inward/record.url?scp=84939940377&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2015.02.033
DO - 10.1016/j.eswa.2015.02.033
M3 - Artículo
SN - 0957-4174
VL - 42
SP - 5177
EP - 5191
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 12
ER -