TY - CHAP
T1 - Acceleration of Path Planning Computation Based on Evolutionary Artificial Potential Field for Non-static Environments
AU - Orozco-Rosas, Ulises
AU - Picos, Kenia
AU - Montiel, Oscar
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.
AB - In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.
KW - Evolutionary artificial potential field
KW - Graphics processing unit
KW - Heterogeneous computing
KW - Mobile robots
KW - Path planning
UR - http://www.scopus.com/inward/record.url?scp=85080882948&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35445-9_22
DO - 10.1007/978-3-030-35445-9_22
M3 - Capítulo
AN - SCOPUS:85080882948
T3 - Studies in Computational Intelligence
SP - 271
EP - 297
BT - Studies in Computational Intelligence
PB - Springer
ER -