An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

K. Rajchandar; R. Baskaran; K. Padmanabhan Panchu; M. Rajmohan

doi:10.14429/dsj.71.16563

K. Rajchandar Department of Industrial Engineering, College of Engineering Campus, Guindy Campus, Anna University, Chennai - 600 025 https://orcid.org/0000-0002-5952-0428
R. Baskaran Department of Industrial Engineering, College of Engineering Campus, Guindy Campus, Anna University, Chennai - 600 025 https://orcid.org/0000-0002-8428-1393
K. Padmanabhan Panchu Department of Industrial Engineering, College of Engineering Campus, Guindy Campus, Anna University, Chennai - 600 025 https://orcid.org/0000-0002-7791-2513
M. Rajmohan Department of Industrial Engineering, College of Engineering Campus, Guindy Campus, Anna University, Chennai - 600 025 https://orcid.org/0000-0002-0693-7748

DOI: https://doi.org/10.14429/dsj.71.16563

Keywords: Optimal path planning, Grid approach, Mobile robot navigation, Multi-objective, Path smoothing, Alternative pathway

Abstract

Currently, automated and semi-automated industries need multiple objective path planning algorithms for mobile robot applications. The multi-objective optimisation algorithm takes more computational effort to provide optimal solutions. The proposed grid-based multi-objective global path planning algorithm [Quadrant selection algorithm (QSA)] plans the path by considering the direction of movements from starting position to the target position with minimum computational effort. Primarily, in this algorithm, the direction of movements is classified into quadrants. Based on the selection of the quadrant, the optimal paths are identified. In obstacle avoidance, the generated feasible paths are evaluated by the cumulative path distance travelled, and the cumulative angle turned to attain an optimal path. Finally, to ease the robot’s navigation, the obtained optimal path is further smoothed to avoid sharp turns and reduce the distance. The proposed QSA in total reduces the unnecessary search for paths in other quadrants. The developed algorithm is tested in different environments and compared with the existing algorithms based on the number of cells examined to obtain the optimal path. Unlike other algorithms, the proposed QSA provides an optimal path by dramatically reducing the number of cells examined. The experimental verification of the proposed QSA shows that the solution is practically implementable.