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Summary Path planning is a fundamental and critical task in many robotic applications. For energy‐constrained robot platforms, path planning solutions are desired with minimum time arrivals and minimal energy consumption. Uncertain environments, such as wind conditions, pose challenges to the design of effective minimum time‐energy path planning solutions. In this article, we develop a minimum time‐energy path planning solution in continuous state and control input spaces using integral reinforcement learning (IRL). To provide a baseline solution for the performance evaluation of the proposed solution, we first develop a theoretical analysis for the minimum time‐energy path planning problem in a known environment using the Pontryagin's minimum principle. We then provide an online adaptive solution in an unknown environment using IRL. This is done through transforming the minimum time‐energy problem to an approximate minimum time‐energy problem and then developing an IRL‐based optimal control strategy. Convergence of the IRL‐based optimal control strategy is proven. Simulation studies are developed to compare the theoretical analysis and the proposed IRL‐based algorithm. 

Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT * (Multi-T-RRT * path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented.