Recharging of Distributed Loads via Schedule Optimization with Autonomous Mobile Energy Assets

As the development and use of multi-agent autonomous systems increases for use in applications such as planetary exploration, military reconnaissance, or microgrid systems, optimized operations needs to be considered in order to maximize the utility of resources. In autonomous mobile systems, mission plans involving path planning, scheduling, and energy management are all of immense concern and priority in operations where energy resources are limited or scarce. An optimization method with the ability to allocate tasks is a valuable tool for use in these systems. Mobile microgrids, with the ability to adapt and reconfigure to better service electrical loads, requires this optimized mission planning. This paper proposes multiple algorithm optimization strategies of task allocation for energy assets in an autonomous mobile sub-microgrid system. The objective is to create an optimal mission plan to navigate to and recharge distributed and fixed electrical loads wirelessly, in order to extend and maximize their operational life. Data collection from sub-mission testing with a Clearpath Husky robotic unmanned ground vehicle is utilized for Monte Carlo simulations to better understand algorithm mission response to variable parameters. The novel results will show that the optimization approach and methods can be regarded as a reliable schedule optimization tool for this application of wireless recharging of loads/subsystems. The proposed approach can be extended to a multitude of applications in mission planning, involving different objectives such as recharging wireless sensor networks, unmanned aerial vehicles, or other UGVs to extend mission operation time.