t-SDBO: A Heterogeneous Multi-Robot Task Allocation Method Based on Chaotic Adaptive Dung Beetle Optimization Algorithm
Abstract
Heterogeneous multi-robot task allocation (MRTA) requires the efficient coordination of robots with different capabilities while balancing task benefits, travel costs, and completion time. To address the challenges of complex constraints and premature convergence in conventional optimization methods, this paper proposes a heterogeneous MRTA algorithm based on chaotic adaptive dung beetle optimization algorithm, termed t-SDBO. First, a task allocation model is established by incorporating time-dependent task benefits, robot resource requirements, and task execution constraints into a weighted combinatorial optimization formulation. A real-valued encoding–decoding mechanism and constraint-repair strategy are then employed to transform continuous search positions into feasible discrete task assignments and execution sequences. To improve the optimization performance of the dung beetle optimizer, Sine chaotic mapping is introduced to enhance initial population diversity, while adaptive t-distribution mutation is used to strengthen exploration and alleviate premature convergence. Furthermore, a dynamic selection strategy adjusts the application of mutation to balance solution quality and computational overhead. Comparative experiments show that t-SDBO achieves an average fitness value of 4.3556 and a best fitness value of 4.3213 in the four-robot, twelve-task scenario, outperforming PSO, SSA, standard DBO, and SDBO in solution quality. In the ROS/Gazebo multi-robot navigation benchmark, t-SDBO reduces the total navigation time by 3.50% and 14.17%, and the total travel distance by 4.72% and 8.36%, compared with DBO and PSO, respectively, while maintaining a 100% task completion success rate. These results demonstrate that the proposed method can improve both optimization quality and multi-robot execution efficiency, providing a practical solution for constrained heterogeneous multi-robot task allocation.