Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Linear robust control
Convex Optimization
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Autonomous behaviors for interactive vehicle animations
Graphical Models - Special issue on SCA 2004
Planning Algorithms
Anytime search in dynamic graphs
Artificial Intelligence
Differentially constrained mobile robot motion planning in state lattices
Journal of Field Robotics - Special Issue on Space Robotics, Part I
International Journal of Robotics Research
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Journal of Artificial Intelligence Research
Flow-through policies for hybrid controller synthesis applied to fully actuated systems
IEEE Transactions on Robotics
A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance
Journal of Intelligent and Robotic Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ACM Transactions on Mathematical Software (TOMS)
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
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A hierarchical approach for motion planning and control of nonlinear systems operating in obstacle environments is presented. To reduce the computation time during the motion planning process, dynamically feasible trajectories are generated in real-time through concatenation of pre-specified motion primitives. The motion planning task is posed as a search over a directed graph, and the applicability of informed graph search techniques is investigated. Specifically, we develop a locally greedy algorithm with effective backtracking ability and compare this algorithm to weighted A* search. The greedy algorithm shows an advantage with respect to solution cost and computation time when larger motion primitive libraries that do not operate on a regular state lattice are utilized. Linearization of the nonlinear system equations about the motion primitive library results in a hybrid linear time-varying model, and an optimal control algorithm using the @?"2-induced norm as the performance measure is provided to ensure that the system tracks the desired trajectory. The ability of the resulting controller to closely track the trajectory obtained from the motion planner, despite various disturbances and uncertainties, is demonstrated through simulation.