Graph-Based Embodied Planning for Heterogeneous Robot Teams in Unstructured Fields
Keywords:
Embodied Artificial Intelligence, Heterogeneous Robotics, Graph-Based Planning, Unstructured Environments, Multi-Agent CoordinationAbstract
The deployment of heterogeneous robot teams in unstructured and dynamic environments remains a formidable challenge in modern robotics. Traditional planning algorithms often fail to account for the unique morphological and sensory capabilities of varying robotic platforms, leading to suboptimal performance in terrains lacking defined topological pathways. This paper presents a novel framework for graph-based embodied planning specifically designed for heterogeneous multi-agent systems operating in unstructured fields. By integrating embodied artificial intelligence principles with dynamic graph-theoretic structures, the proposed methodology allows individual agents, such as unmanned aerial vehicles, legged robots, and wheeled ground vehicles, to construct, share, and navigate a unified semantic-topological representation of their environment. This shared graph dynamically updates edge weights based on the localized, embodied perception of each agent, thereby reflecting real-time traversability and task-specific affordances. Through decentralized coordination mechanisms, the system achieves efficient cross-platform task allocation and collision-free trajectory generation. Extensive simulation results demonstrate that the proposed graph-based embodied approach significantly reduces computational overhead and planning latency while improving mission success rates compared to conventional multi-agent pathfinding methodologies. The framework provides a robust foundation for applications in disaster response, planetary exploration, and automated agriculture, where environmental unpredictability necessitates highly adaptive and cooperative robotic solutions.
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