Fully distributed singularity-free prescribed-time stabilization of the continuous-time generalized adaptive Bellman-Ford algorithm

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Building upon the well-established distributed biased min-consensus protocol, which serves as an efficient approach to address the shortest path problem in a distributed fashion, the continuous-time generalized adaptive Bellman-Ford algorithm (GABF) introduces flexibility by accommodating various forms of distance metrics. This adaptability makes GABF suitable for more complex scenarios, such as time-dependent shortest path problem and robotic path planning. However, existing research on this protocol primarily focuses on asymptotic stability, providing no insights into convergence speed, which limits its practical applications. To address this gap, this paper proposes two control strategies that achieve prescribed-time stabilization of GABF by ensuring its convergence to the stationary value within a user-defined time, thereby broadening its applicability. Simulation scenarios, including robotic manipulator path planning with real-world data and learning-based path planning, are provided to validate the effectiveness of the proposed approaches.

Links and sources

• Official / arXiv page
• PDF from original source

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