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This paper introduces a distributed task planning method for robot swarms executing heterogeneous tasks in uncertain environments, addressing limitations of existing methods related to task modeling, environmental sensitivity, and deadlock risks. The approach uses a Conditional Value-at-Risk (CVaR) model for dynamic task time estimation, transforms multi-geometry tasks into equivalent point-task sets, and embeds a cooperative task dependency graph into a distributed auction consensus mechanism with cycle detection. Simulation and real-world UAV tests demonstrate the method's superiority in completion time, success rate, and efficiency, confirming its robustness in large-scale scenarios.
Robot swarms can now handle complex, synchronized tasks in uncertain environments without getting stuck, thanks to a new planning method that dynamically adapts to risk and prevents deadlocks.
Aiming at the challenges in planning for the synchronized execution of multiple geometrically heterogeneous tasks by swarms in uncertain environments, this work proposes a distributed task planning method. This method is intended to overcome the inefficiencies in traditional methods caused by fragmented task modeling, high sensitivity to environmental uncertainty, and the risk of collaborative deadlocks. First, a Conditional Value-at-Risk (CVaR) model is introduced to dynamically estimate task execution time under uncertainty. Second, multi-geometry tasks (area, line, point) are uniformly transformed into equivalent point-task sets via grid-based decomposition and key-point discretization. Finally, a cooperative task dependency graph is embedded into a distributed auction consensus mechanism with cycle detection to prevent deadlocks, and an adaptive cooperative obstacle avoidance strategy based on task progress is integrated to ensure synchronized and collision-free motion. Simulation results demonstrate our method鈥檚 superiority over benchmarks in completion time, success rate, and efficiency. Its practicality is validated through real-world UAV tests, while scalability experiments confirm its robustness in large-scale scenarios. This work provides a solid theoretical foundation and technical support for efficient coordination of robot swarms in complex and dynamic environments. Note to Practitioners鈥擠istributed task planning in multi-robot systems is essential for practical applications such as disaster response, environmental monitoring, and infrastructure inspection. These scenarios often involve uncertainties, diverse task geometries, and strict synchronization constraints, posing significant challenges to conventional algorithms. This paper introduces a robust and scalable planning framework that incorporates dynamic risk assessment, deadlock immunity, and cooperative motion planning. The proposed method supports efficient and safe execution of synchronized heterogeneous tasks under complex environmental conditions. Practitioners can adopt this approach to improve the coordination of autonomous aerial or ground vehicle swarms, particularly in scenarios where real-time responsiveness and high task success rates are critical. Future work may focus on hardware integration and communication robustness for field deployment.
