A Collaborative Task Allocation and Path Planning Method for Multi-Unmanned Vehicles Based on Dynamic Energy Consumption Optimization

To address the insufficient optimization of energy consumption in task allocation and path planning for multiunmanned vehicle systems operating in dynamic environments, this paper proposes a collaborative method based on dynamic energy consumption optimization. Traditional approaches typically decouple task allocation and path planning, neglecting the nonlinear impacts of dynamic factors on energy efficiency, which leads to suboptimal global performance. This study constructs a dynamic energy perception joint optimization framework by designing a closed-loop interaction mechanism between discrete task allocation and continuous path planning layers. The dynamic coupling effects of payload, gradient, and velocity are unified into energy consumption model of unmanned vehicles, the energy consumption prediction accuracy of unmanned vehicle is improved. A bidirectional feedback mechanism achieves global coordination, while a bi-objective optimization function with a hybrid scoring strategy simultaneously minimizes total system energy consumption and balances battery levels. Simulation experiments demonstrate that the framework can better accomplish the task assignment and path planning of unmanned vehicles, validating its effectiveness and robustness in logistics transportation and similar scenarios.