Robot Motion Planning
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Planning Algorithms
Creating High-quality Paths for Motion Planning
International Journal of Robotics Research
Disassembly Path Planning for Complex Articulated Objects
IEEE Transactions on Robotics
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
Real-time navigation using randomized kinodynamic planning with arrival time field
Robotics and Autonomous Systems
Terrain traversability analysis methods for unmanned ground vehicles: A survey
Engineering Applications of Artificial Intelligence
Planning stable paths for urban search and rescue robots
Robot Soccer World Cup XV
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This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.