Dynamic Obstacle Avoidance for Multi-Robot HPB-CBS under Heterogeneous Motion Constraints.

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Title:	Dynamic Obstacle Avoidance for Multi-Robot HPB-CBS under Heterogeneous Motion Constraints.
Authors:	Gao, Changqing¹ gcq@hnu.edu.cn, Fang, Qiu² qfang@hnu.edu.cn, Zeng, Kui³ zk101@hnu.edu.cn, Lei, Haipeng⁴ 446430855@qq.com, Jia, Zhihao⁵ jia13220716491@hnu.edu.cn, Liu, Xiaorui⁶ liuxiaorui@hnu.edu.cn
Source:	Engineering Letters. Jun2026, Vol. 34 Issue 6, p2374-2384. 11p.
Subjects:	Obstacle avoidance (Robotics), Robotic path planning, Scheduling, Robot kinematics
Abstract:	To address heterogeneous kinematic models and dynamic task sequences in multi-agent path finding (MAPF) for heterogeneous multi-robot systems, this paper proposes a Heterogeneous Priority Batching Conflict-Based Search (HPBCBS) algorithm. We develop a dynamic heterogeneous priority model that quantitatively ranks robots by jointly considering obstacle-avoidance capability, task urgency, and sequence utility. Building on this model, we enhance the CBS framework with priority-aware conflict selection and a robot-body-based continuous-space conflict definition at the high level, and an improved hybrid A* planner that supports both Ackermannsteered and holonomic robots at the low level. Moreover, a priority-based batching strategy and a hybrid online scheduling mechanism that combines event-triggered and periodic replanning are introduced to handle dynamic changes. Simulation results show that, compared with CBS-BS2 and PP-SHA*, HPBCBS achieves higher success rates across diverse problem sizes and obstacle densities, while improving solution quality and scalability. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	To address heterogeneous kinematic models and dynamic task sequences in multi-agent path finding (MAPF) for heterogeneous multi-robot systems, this paper proposes a Heterogeneous Priority Batching Conflict-Based Search (HPBCBS) algorithm. We develop a dynamic heterogeneous priority model that quantitatively ranks robots by jointly considering obstacle-avoidance capability, task urgency, and sequence utility. Building on this model, we enhance the CBS framework with priority-aware conflict selection and a robot-body-based continuous-space conflict definition at the high level, and an improved hybrid A* planner that supports both Ackermannsteered and holonomic robots at the low level. Moreover, a priority-based batching strategy and a hybrid online scheduling mechanism that combines event-triggered and periodic replanning are introduced to handle dynamic changes. Simulation results show that, compared with CBS-BS2 and PP-SHA*, HPBCBS achieves higher success rates across diverse problem sizes and obstacle densities, while improving solution quality and scalability. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X