Path planning of mobile robot in dynamic environments

Abstract

To overcome the drawbacks of traditional methods for robot path planning in dynamic unknown environment, a hybrid planning approach composing of global planning and local planning is proposed. In global planning, modified ant colony optimization (MACO) algorithm with early death strategy is proposed to overcome the local optimal and U or V-shaped obstacles. MACO is used to plan a rough global optimal path off-line, and decompose the rough path into many sub-targets for local planning. In local planning, robot detects the local environment information on-line. Robot corrects its path using rolling optimization principle and moves towards the sub-targets. It transfers the global optimization into local optimization in rolling windows. Simulation results show its effectiveness and good stability.