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This research advances autonomous navigation by introducing adaptive kinematic switching, allowing robots to dynamically adjust their motion models based on terrain and state for optimal efficiency. Validated on quadrupeds and drones, the Heading-Aware A* algorithm combined with multi-model pure pursuit demonstrates robust performance across diverse environments without relying on central AI training but rather on control-theoretic innovations.
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Global Path Planner with Multi-Model Switching

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arXiv:2609.13015v1 Announce Type: new Abstract: This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A* algorithm for generating feasible paths, and a multi-model Pure Pursuit controller for dynamic tracking. A core innovation is adaptive kinematic modeling, enabling real-time switching between kinematic models based on terrain features and robot states. This adaptability optimizes path efficiency and energy use in challenging scenarios. We validate the approach in simulation on different platforms, namely the Artaban quadruped and the X3 quadrotor drone, showcasing improved performance, robustness, and adaptability over standard baselines.

Abstract:This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A* algorithm for generating feasible paths, and a multi-model Pure Pursuit controller for dynamic tracking. A core innovation is adaptive kinematic modeling, enabling real-time switching between kinematic models based on terrain features and robot states. This adaptability optimizes path efficiency and energy use in challenging scenarios. We validate the approach in simulation on different platforms, namely the Artaban quadruped and the X3 quadrotor drone, showcasing improved performance, robustness, and adaptability over standard baselines.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.13015 [cs.RO]

(or arXiv:2609.13015v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.13015 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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