🤖 AI Summary
To address the limited path-planning flexibility of four-wheel independent-steering (4WIS) robots in complex environments caused by single-kinematic-model abstractions, this paper proposes a multimodal-fusion Hybrid A* planning framework. The method constructs a unified four-dimensional state space to jointly represent diverse steering modes and designs a multimodal Reeds–Shepp curve set supporting forward/backward motion under differential, Ackermann, and omnidirectional kinematics. A mode-switching cost-aware heuristic function is introduced to guide search efficiently, and an intelligent terminal connection strategy enables optimal mode selection and seamless trajectory stitching. Experimental results demonstrate that the proposed approach significantly improves planning success rate and computational efficiency in narrow, dynamic environments, while enhancing motion adaptability and environmental robustness. This work establishes a scalable, multimodal planning paradigm for autonomous navigation of 4WIS platforms.
📝 Abstract
Four-wheel independent steering (4WIS) systems provide mobile robots with a rich set of motion modes, such as Ackermann steering, lateral steering, and parallel movement, offering superior maneuverability in constrained environments. However, existing path planning methods generally assume a single kinematic model and thus fail to fully exploit the multi-modal capabilities of 4WIS platforms. To address this limitation, we propose an extended Hybrid A* framework that operates in a four-dimensional state space incorporating both spatial states and motion modes. Within this framework, we design multi-modal Reeds-Shepp curves tailored to the distinct kinematic constraints of each motion mode, develop an enhanced heuristic function that accounts for mode-switching costs, and introduce a terminal connection strategy with intelligent mode selection to ensure smooth transitions between different steering patterns. The proposed planner enables seamless integration of multiple motion modalities within a single path, significantly improving flexibility and adaptability in complex environments. Results demonstrate significantly improved planning performance for 4WIS robots in complex environments.