The complexity of robot motion planning
The complexity of robot motion planning
Robot Analysis and Design: The Mechanics of Serial and Parallel Manipulators
Robot Analysis and Design: The Mechanics of Serial and Parallel Manipulators
Planning Algorithms
Motion Planning for a Class of Planar Closed-chain Manipulators
International Journal of Robotics Research
GNU Scientific Library Reference Manual - Third Edition
GNU Scientific Library Reference Manual - Third Edition
A linear relaxation technique for the position analysis of multiloop linkages
IEEE Transactions on Robotics
Manipulation planning on constraint manifolds
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Path planning in 1000+ dimensions using a task-space Voronoi bias
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Reachable Distance Space: Efficient Sampling-Based Planning for Spatially Constrained Systems
International Journal of Robotics Research
Sampling-based path planning on configuration-space costmaps
IEEE Transactions on Robotics
Global manipulation planning in robot joint space with task constraints
IEEE Transactions on Robotics
Synthesizing grasp configurations with specified contact regions
International Journal of Robotics Research
Task Space Regions: A framework for pose-constrained manipulation planning
International Journal of Robotics Research
Improving Motion-Planning Algorithms by Efficient Nearest-Neighbor Searching
IEEE Transactions on Robotics
A Relational Positioning Methodology for Robot Task Specification and Execution
IEEE Transactions on Robotics
Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach
International Journal of Robotics Research
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Despite the significant advances in path planning methods, highly constrained problems are still challenging. In some situations, the presence of constraints defines a configuration space that is a non-parametrizable manifold embedded in a high-dimensional ambient space. In these cases, the use of sampling-based path planners is cumbersome since samples in the ambient space have low probability to lay on the configuration space manifold. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. The proposed planner builds on recently developed tools for higher-dimensional continuation, which provide numerical procedures to describe an implicitly defined manifold using a set of local charts. We propose to extend these methods focusing the generation of charts on the path between the two configurations to connect and randomizing the process to find alternative paths in the presence of obstacles. The advantage of this planner comes from the fact that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.