2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022 · 19 citations · 26 references
ComputingAutonomous SystemsRobotics EngineeringAerial RoboticsArtificial IntelligencePath PlanningExploration-exploitation TradeoffNavigation PlanningOptimal ControlPolicy LearningReinforcement LearningTrajectory GenerationAutonomous Vehicle NavigationFlight Path PlanningOffline Reinforcement LearningQ-learningReinforcement Learning For NavigationReinforcement Learning For RoboticsRobot Path PlanningTrajectory Planning
Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Help from the Sky: Leveraging UAVs for Disaster Management
Milan Erdelj, Enrico Natalizio, Kaushik Chowdhury et al. · IEEE Pervasive Computing · 2017 · 992 citations