This paper examines an integrated path planning and power management problem for a solar-powered unmanned ground vehicle (UGV). The proposed method seeks to minimize the travel time of the UGV through an area of known energy density by designing a smooth, heuristically optimized path and allocating the vehicle's power among its electrical components, while the UGV harvests ambient energy along the designed path to satisfy with the mission's strict energy constraints. A scalar field is first established to evaluate the solar radiation density at discrete locations. A modified particle swarm optimization method is applied to search for a minimal time path wherein the energy gathered is equal to or greater than the energy expended. The proposed modeling and optimization strategy is verified through computer simulation and experimental demonstration.

This paper examines an integrated path planning and power management problem for a solar-powered unmanned ground vehicle (UGV). The proposed method seeks to minimize the travel time of the UGV through an area with a known energy density by designing an optimal path and allocating the vehicle's power among its electrical components, while the UGV operates under strict power constraints and harvests ambient environmental energy along the designed path. A scalar field is first established to evaluate the solar radiation density at discrete locations. A modified Particle Swarm Optimization method is applied to search for a minimal time path wherein the energy gathered is equal to or greater than the energy expended. The proposed modeling and optimization strategy is verified through computer simulation and experimental demonstration.