Mobile robotics research and deployment is highly challenged by energy limitations, particularly in marine robotics applications. This challenge can be addressed by autonomous transfer and sharing of energy in addition to effective mission planning. Specifically, it is possible to overcome energy limitations in robotic missions using an optimization approach that can generate trajectories for both working robots and mobile chargers while adapting to environmental changes. Such a method must simultaneously optimize all trajectories in the robotic network to be able to maximize overall system efficiency. This letter presents a Genetic Algorithm based approach that is capable of solving this problem at a variety of scales, both in terms of the size of the mission area and the number of robots. The algorithm is capable of re-planning during operation, allowing for the mission to adapt to changing conditions and disturbances. The proposed approach has been validated in multiple simulation scenarios. Field experiments using an autonomous underwater vehicle and a surface vehicle verify feasibility of the generated trajectories. The simulation and experimental validation show that the approach efficiently generates feasible trajectories to minimize energy use when operating multi-robot networks.

中文翻译：

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考虑到能源限制的长期运行的协同任务规划

移动机器人研究和部署受到能源限制的高度挑战，特别是在海洋机器人应用中。除了有效的任务规划外，还可以通过自主传输和共享能量来解决这一挑战。具体来说，可以使用优化方法来克服机器人任务中的能量限制，该方法可以在适应环境变化的同时为工作机器人和移动充电器生成轨迹。这种方法必须同时优化机器人网络中的所有轨迹，才能最大限度地提高整体系统效率。这封信提出了一种基于遗传算法的方法，该方法能够在各种尺度上解决这个问题，无论是任务区域的大小还是机器人的数量。该算法能够在运行过程中重新规划，使任务能够适应不断变化的条件和干扰。所提出的方法已在多个模拟场景中得到验证。使用自主水下航行器和水面航行器的现场实验验证了生成轨迹的可行性。仿真和实验验证表明，该方法有效地生成可行的轨迹，以在操作多机器人网络时最大限度地减少能源使用。