Abstract
To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.
Highlights
The Unmanned Aerial Vehicles (UAVs) are widely used in the military battlefield nowadays, where the military practices are often challenged with more and more complex situations in contested tactic environments
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Summary
The Unmanned Aerial Vehicles (UAVs) are widely used in the military battlefield nowadays, where the military practices are often challenged with more and more complex situations in contested tactic environments. A Consensus-Based Bundle Algorithms (CBBA) is developed further to solve the multi-task assignment problem. In [22], researchers Hunt et al further improved CBBA to a Consensus Based Group Algorithms (CBGA) They set up each task to be resolved by multiple collaborated UAVs. They set up each task to be resolved by multiple collaborated UAVs This method can improve 50% optimality of the solution but perform only single type of task. This algorithm takes part of the assigned tasks from the allocation state, and mixes them with new tasks for reallocation purposes They achieve the purpose of ensuring optimality and real-time performance in dynamic allocations, but the types of tasks allocated are single. The structure of this paper is organized as follows: Section 2 presents the problem description and mathematical modeling; Section 3 discusses the task-planning algorithm; Section 4 simulates and validates the algorithm; Section 5 summarizes the work
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