This paper focuses on surveillance mission planning and power management of a solar-powered Unmanned Ground Vehicle (UGV). The task of the UGV is to maximize the exploration of a unknown region to gather information from the environment via on-board equipment. Meanwhile, the UGV is required to utilize the ambient solar energy from the environment and operate under specified net energy constraint within a limited traveling time. The modified Particle Swarm Optimization (PSO) and Rapidly-Exploring Random Tree (RRT) methods are proposed to search for a locally optimized path for the assigned mission. The resulted mission plans obtained from both methods are demonstrated through computer simulation and experimental testing.
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