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. Note to Practitioners-With the advancement of autonomous technology, robotic systems have alleviated human operators from numerous tedious tasks where system endurance plays a crucial role. Current technology employed in solar-powered robotic systems is subject to design and power limitations and varying environments. Intelligently harvesting energy from environments and scheduling power consumption to optimize desired system performance will significantly improve the solar robot's endurance. Therefore, the goal of realizing energy autonomy of solar-powered robotic systems motivates this work. Any robotic system which can access solar energy will potentially benefit from the results of this work. With solar panels integrated into robotic systems applied to missions such as environmental monitoring, search and rescue, surveillance, and farming, the proposed path planning and power management strategy has the potential to dramatically extend the operation time of a system.