A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms.

Area of Science:

• Robotics and Autonomous Systems
• Multi-Agent Systems
• Underwater Navigation and Control

Background:

• Task allocation in heterogeneous unmanned underwater vehicle (UUV) swarms presents significant challenges.
• Real-world underwater environments impose communication range constraints, complicating dynamic task allocation.

Purpose of the Study:

• To propose a novel dynamic extended consensus-based bundle algorithm (DECBBA) for efficient task allocation in UUV swarms.
• To address multi-UUV task allocation where individual UUVs can perform multiple tasks, considering time, path, and voyage costs.
• To optimize load balance and achieve performance close to ideal scenarios despite communication constraints.

Main Methods:

• Development of the dynamic extended consensus-based bundle algorithm (DECBBA).
• Construction of a "UUV-task" matching matrix.
• Design of new marginal utility, reward, and cost functions accounting for time, path, and UUV voyage.
• Implementation of discrete time-stage updates for UUV status and task completion.

Main Results:

• The DECBBA effectively manages dynamic task allocation for UUV swarms under communication constraints.
• Performance indicators, including global utility and task completion rate, approach those of static algorithms in ideal conditions.
• Simulation results demonstrate quick and efficient generation of dynamic, conflict-free task assignments.

Conclusions:

• The proposed DECBBA provides a robust solution for dynamic task allocation in heterogeneous UUV swarms.
• The algorithm effectively overcomes communication limitations in underwater environments.
• DECBBA achieves high performance in terms of efficiency, load balancing, and task completion rates.