hpp-core
4.10.1
Implement basic classes for canonical path planning for kinematic chains.
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Configuration Sampling | Random sampling of robot configurations for random path planning |
Path planning algorithms | Path planning algorithms derive from class hpp::core::PathPlanner |
Path Optimization | Path optimization algorithms derive from class hpp::core::PathOptimizer |
Steering method and distance functions | Some system are subject to kinematic or dynamic constraints. Those constraints can be handled in path planning using a steering method that builds an admissible path between two configurations of the system. When using a steering method, it can be useful to use a distance function that accounts for the cost to go from a configuration to another |
Validation of configurations and paths | Paths and configurations need to be validated with respect to various criteria (collision, joint bounds,...) during path planning and path optimization. Validation of a configuration or of a path gives rise to a validation report that provide information if validation failed |
Roadmap | Random sampling algorithm build a representation of the free configuration space as a graph called a roadmap. Nodes are configurations (or states) and edges are collision-free admissible paths (or trajectories) |
Constraints | Some robots can be subject to constraints |
Path | |
Parser | |
Plugins |