A multi-objective grey wolf optimizer for grid-based mobile robot path planning with turn-aware optimization

Tawfik Guesmi; Nandhini Mahadevan; Dinesh Karunanidy; Khalid Alqunun; Tawfik Guesmi; Nandhini Mahadevan; Dinesh Karunanidy; Khalid Alqunun

doi:10.3934/math.2026488

AIMS Mathematics

2026, Volume 11, Issue 4: 11882-11923. doi: 10.3934/math.2026488

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A multi-objective grey wolf optimizer for grid-based mobile robot path planning with turn-aware optimization

1.
Department of Electrical Engineering, College of Engineering, University of Ha'il, Ha'il 2240, Saudi Arabia; tawfik.guesmi@istmt.rnu.tn (T.G.); kh.alqunun@uoh.edu.sa (K.A.)
2.
Department of Computer Science and Engineering (Data Science), Madanapalle Institute of Technology & Science (MITS), Deemed to be University, Madanapalle 517325, India; nandynew24@gmail.com (N.M.)
3.
Department of Computer Science & Technology, Madanapalle Institute of Technology & Science (MITS), Deemed to be University, Madanapalle 517325, India; dineshhumar@gmail.com (D.K.)

Received: 01 March 2026 Revised: 14 April 2026 Accepted: 16 April 2026 Published: 28 April 2026
MSC : 90C59, 90C29, 90C35, 68T40

Grid-based mobile robot path planning often requires a trade-off between path efficiency and maneuver regularity when obstacle force detours and trigger heading changes. This paper proposes a multi-objective grey wolf optimizer for grid-based path planning with turn-aware optimization. The path length and maneuver burden are optimized simultaneously using an 8-connected motion model with Euclidean-consistent costs and a strict no-corner-cutting feasibility rule. The experimental analysis is organized in three levels: A basic benchmark specification with 9-map environments, a structured 60-map benchmark covering five grid sizes, four obstacle-density levels, and three obstacle morphologies, and sequential qualitative visualization of dynamic obstacle avoidance and multi-robot coordination. The proposed method consistently achieves the lowest mean path length and the lowest mean turn count among the compared methods. Statistical comparison further shows significant gains in path length and turning burden on the majority of benchmark environments. The results indicate that the proposed method preserves favorable path-quality trade-offs across increasing grid size, obstacle density, and structural complexity, while also exhibiting coherent behavior in dynamic avoidance and coordination scenarios.
- mobile robot path planning,
- occupancy grid,
- grey wolf optimizer,
- multi-objective optimization,
- turn minimization
Citation: Tawfik Guesmi, Nandhini Mahadevan, Dinesh Karunanidy, Khalid Alqunun. A multi-objective grey wolf optimizer for grid-based mobile robot path planning with turn-aware optimization[J]. AIMS Mathematics, 2026, 11(4): 11882-11923. doi: 10.3934/math.2026488

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Abstract

Grid-based mobile robot path planning often requires a trade-off between path efficiency and maneuver regularity when obstacle force detours and trigger heading changes. This paper proposes a multi-objective grey wolf optimizer for grid-based path planning with turn-aware optimization. The path length and maneuver burden are optimized simultaneously using an 8-connected motion model with Euclidean-consistent costs and a strict no-corner-cutting feasibility rule. The experimental analysis is organized in three levels: A basic benchmark specification with 9-map environments, a structured 60-map benchmark covering five grid sizes, four obstacle-density levels, and three obstacle morphologies, and sequential qualitative visualization of dynamic obstacle avoidance and multi-robot coordination. The proposed method consistently achieves the lowest mean path length and the lowest mean turn count among the compared methods. Statistical comparison further shows significant gains in path length and turning burden on the majority of benchmark environments. The results indicate that the proposed method preserves favorable path-quality trade-offs across increasing grid size, obstacle density, and structural complexity, while also exhibiting coherent behavior in dynamic avoidance and coordination scenarios.

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