Robust online multi-robot path planning for complex environments
Year: 2026
Type: article
Abstract: This work develops an adaptive enhanced framework to address slow convergence to high-quality trajectories, insufficient path smoothness, and elevated collision risk in multi-robot path planning. The approach is embedded in a multi-constraint, single-objective optimization model that incorporates kinematic limits, static and dynamic obstacle avoidance, and practical mission cost. Three complementary mechanisms are introduced: a velocity-alignment coordination to accelerate cooperative convergence of candidate solutions; an adaptive follow escape probability strategy that dynamically balances global exploration and local refinement to produce smoother and safer paths; and an adaptive random-perturbation mechanism to escape local optima and increase robustness in complex scenarios. Benchmark and simulation studies show that the proposed framework outperforms baseline and state-of-the-art comparison methods in terms of convergence accuracy, convergence speed, computational efficiency, path quality, and safety, providing a practical and robust solution for improving autonomy and operational performance in complex engineering multi-robot systems.
Source: Ain Shams Engineering Journal
Authors: Zheng Liu, Wan Xu, Yujie Wang, Xiaochi Mu
Institution: Hubei University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7151039338