Mixed-type decision-making is ubiquitously required in robotic systems and has attracted significant research interests. Examples include, but not limited to, the integrated task and motion planning and optimal control of hybrid systems involving both continuous and discrete dynamic behaviors. For decision-making of robotic systems to improve operational efficiency, safety, and/or mission success rate, they involve both discrete variables representing task allocation or transitions between discrete modes and continuous variables representing trajectories of the planned motion or states governed by differential equations. This paper formulates a class of mixed-type decision-making problems with polynomial objective and constraints as quadratically constrained quadratic programming (QCQP) problems and a nonconvex optimization method based on alternating direction method of multipliers is proposed to solve the QCQP. The proposed optimization method consists of three sequential subproblems, all of which admit closed-form solutions. Moreover, convergence proof of the optimization algorithm is provided. Two representative problems, traveling salesman with obstacle avoidance and rendezvous and docking of a charging station with distinct phase constraints, are described and solved via the proposed method. Numerical simulations as well as experimental verification of both problems are presented and compared with a state-of-art method to validate the effectiveness, efficacy and robustness of the nonconvex optimization method.

This paper presents the design, modeling, control, and experimental test of a solar-powered quadcopter to allow for long-endurance missions. We first present the design of a large-scale quadcopter that incorporates solar energy harvesting capabilities. Based on the design results, we built the dynamical model of the customized quadcopter with analysis of the aerodynamic influence. A feedback control system is developed for the solar-powered quadcopter that takes into account the wind disturbance and is verified in virtual simulation examples. All parameters used in the modeling and simulations are based on a developed prototype of the solar-powered quadcopter. Flight tests with the prototype are presented to validate the feasibility and theoretical basis of the solar-powered quadcopter.

This paper examines techniques for real-time terrain classification and solar irradiance mapping for outdoor, solar-powered mobile robots using a vision-based Artificial Neural Network (ANN). This process is completed sequentially. First, terrain classification is completed by extracting key features from visual-spectrum images captured from an on-board camera using Haar wavelet transform to identify both color and textural information. These features are then classified using an ANN to identify grass, concrete, asphalt, gravel, and mulch. Using the terrain classes, the image is then analyzed using concepts from high dynamic range imagery to establish the solar irradiance map of the area. In this way, our sequential methodology presented allows unmanned vehicles to classify the terrain and map the irradiance of a given area with no prior knowledge. Whereas, the terrain classification can be used in determining energy consumption or traversability criteria and the irradiance map can be used to estimate the energy harvesting capabilities.

This paper presents a mission planning problem for a cooperative team of unmanned ground vehicles (UGVs), which includes multiple rovers and a solar-powered mobile charging station. The team is required to start at an initial point and visit a series of objective points before arriving at the final point selected from the set of objective points, where the UGVs will be charged from the solar-powered mobile charging station. This mission is represented as a multi-Hamiltonian Path Problem (mHPP). In order to effectively coordinate the team, an understanding of the mission environment is first obtained by generating a scalar field representation of the solar insolation of the environment from a visual-spectrum image. Then, a cascaded heuristic optimization algorithm, using modified genetic algorithm and particle swarm optimization, is used to generate a time-optimized mission plan for the team of UGVs, which guides each UGV to its assigned objective points and then rendezvous at the final charging location while guaranteeing compliance with the net energy gain constraint. The feasibility and efficiency of the proposed algorithm are verified using an experimental testbed and constructed indoor simulation environments.

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 a mission planning problem for a solar-powered unmanned ground vehicle (UGV) which requires the vehicle to visit a series of objective points in minimal time subject to a strict net-energy change constraint. Though related to the Traveling Salesperson Problem, the mission planning problem discussed herein imposes further complexity through additional coupled mixed-variable sets and the strict energy constraint. A scalar field representing the solar radiation of the mission environment is first characterized from a visual-spectrum image. A cascaded particle swarm optimization algorithm, coupled with the integer linear programming technique, is used to generate a time-optimized motion plan and power schedules for the UGV, which guides it to visit the assigned objective points with optimized sequence and paths, and then return to its starting location and orientation while guaranteeing compliance with the net energy gain constraint.

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.