Energy-Constrained Motion Planning and Scheduling for Autonomous Robots in Complex Environments
Zhichao Ma, Aijia Sun, Zheyu Zhang, Yuchen Yang, Zijun Gao, Hao Liu
Abstract
Autonomous robots increasingly operate in complex and dynamic environments where energy resources are limited. Effective motion planning and scheduling strategies are critical to achieving mission objectives while minimizing energy consumption. This paper proposes an energy-constrained motion planning framework that jointly considers path optimization and task scheduling under limited energy budgets. We integrate an energy consumption model with a task-priority-based scheduling algorithm to ensure efficient execution of tasks while maintaining safety and mission performance. The proposed approach is validated in simulated complex environments with varying obstacle distributions and energy constraints, demonstrating improved performance compared to baseline greedy and shortest-path-only planners. The results highlight the importance of coupling energy-aware planning with adaptive scheduling to enhance the autonomy of resource-constrained robotic systems.