On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles
Journal of Intelligent and Robotic Systems
An Efficient Path Planning and Control Algorithm for RUAV's in Unknown and Cluttered Environments
Journal of Intelligent and Robotic Systems
Bridging the sense-reasoning gap: DyKnow - Stream-based middleware for knowledge processing
Advanced Engineering Informatics
On the generation of feasible paths for aerial robots in environments with obstacles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Feasible UAV path planning using genetic algorithms and Bézier curves
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
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In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.