TY - GEN
T1 - Mobile Robot Path Planning using Multi-Objective Adaptive Ant Colony Optimization
AU - Agrawal, Rajat
AU - Singh, Bharat
AU - Kumar, Rajesh
AU - Vijayvargiya, Ankit
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Path planning for a mobile robot is a vital task for navigating in a complex environment. However, the path planning problem is challenging because of its non-deterministic polynomial-time (NP) character. In this research, the authors have proposed the Multi-objective Adaptive Ant Colony Optimization for path planning of mobile robot in a static object environment on the grid. The heuristic information function in the conventional ant colony optimization is modified according to the A∗ algorithm, which helps in mitigating the slow convergence of the traditional algorithm. It is because the ants will choose the nodes which are closer to the goal position. Additionally, an objective function is formulated as a multiple objective problem by incorporating (a) Path length, (b) Safety factor, and (c) Energy consumption. Simulation results show that the proposed modification helps to achieve the optimal path in a quicker time i.e., 1.3 times faster than the traditional counterpart.
AB - Path planning for a mobile robot is a vital task for navigating in a complex environment. However, the path planning problem is challenging because of its non-deterministic polynomial-time (NP) character. In this research, the authors have proposed the Multi-objective Adaptive Ant Colony Optimization for path planning of mobile robot in a static object environment on the grid. The heuristic information function in the conventional ant colony optimization is modified according to the A∗ algorithm, which helps in mitigating the slow convergence of the traditional algorithm. It is because the ants will choose the nodes which are closer to the goal position. Additionally, an objective function is formulated as a multiple objective problem by incorporating (a) Path length, (b) Safety factor, and (c) Energy consumption. Simulation results show that the proposed modification helps to achieve the optimal path in a quicker time i.e., 1.3 times faster than the traditional counterpart.
UR - http://www.scopus.com/inward/record.url?scp=85152408119&partnerID=8YFLogxK
U2 - 10.1109/PEDES56012.2022.10080720
DO - 10.1109/PEDES56012.2022.10080720
M3 - Conference contribution
AN - SCOPUS:85152408119
T3 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
BT - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
Y2 - 14 December 2022 through 17 December 2022
ER -