Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Hybrid A* Path Planning with Multi-Modal Motion Extension for Four-Wheel Steering Mobile Robots

Runjiao Bao, Lin Zhang, Tianwei Niu, Haoyu Yuan, Shoukun Wang

TL;DR

The paper tackles global path planning for four-wheel independent steering robots by extending Hybrid A* to a 4D state space that explicitly includes motion mode. It introduces multi-modal Reeds-Shepp curves and a mode-aware heuristic, along with a terminal connection strategy to seamlessly coordinate transitions between Ackermann, lateral, and parallel modes. Empirical results in simulated maze and parking environments, plus preliminary physical experiments, show consistent improvements in path length and cost due to multimodal planning and mode-switch optimization. The approach enhances maneuverability in constrained environments and lays groundwork for future back-end optimization and real-world deployment.

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.

Hybrid A* Path Planning with Multi-Modal Motion Extension for Four-Wheel Steering Mobile Robots

TL;DR

The paper tackles global path planning for four-wheel independent steering robots by extending Hybrid A* to a 4D state space that explicitly includes motion mode. It introduces multi-modal Reeds-Shepp curves and a mode-aware heuristic, along with a terminal connection strategy to seamlessly coordinate transitions between Ackermann, lateral, and parallel modes. Empirical results in simulated maze and parking environments, plus preliminary physical experiments, show consistent improvements in path length and cost due to multimodal planning and mode-switch optimization. The approach enhances maneuverability in constrained environments and lays groundwork for future back-end optimization and real-world deployment.

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.

Paper Structure

This paper contains 15 sections, 13 equations, 6 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Global Path Planning Problem Formulation
  4. 4WIS Kinematic Model
  5. 4WIS Motion Modes
  6. Methodology
  7. Review of the Hybrid A* Algorithm
  8. Multi-Modal State Representation
  9. Multi-Modal Reeds-Shepp Curves
  10. Cost and Heuristic Functions
  11. Terminal Connection Strategy
  12. Experiments
  13. Experimental Setup
  14. Performance Comparison
  15. Conclusion

Figures (6)

  • Figure 1: Configuration of the 4WIS robot.
  • Figure 2: Multi-modal motion sampling. (a) Ackermann steering. (b) Lateral steering. (c) Parallel movement.
  • Figure 3: Visualization of multi-Modal Reeds-Shepp curves.
  • Figure 4: Path planning results in maze environment using baseline Hybrid A* and the proposed extended Hybrid A*.
  • Figure 5: Path planning results in parking environment using baseline Hybrid A* and the proposed extended Hybrid A*.
  • ...and 1 more figures