° Introduction: the various classes of path planning and trajectory planning and their applications.
° Useful concepts: paths, trajectories, configuration space, localisation, mapping, computational complexity, completeness, online vs offline planning, obstacles in configuration space.
° Trajectory planning for robot manipulators:
° Motion planning in configuration space vs in task space.
° Normalized joint variables.
° Polynomial interpolation (linear, third order, fifth order).
° Non polynomial interpolators (bang-bang, trapezoidal).
° Configuration space continued: definitions, dimension, topology.
° Planning in finite spaces: data structures (tree, graph, valued graph) and their traversal algorithms, minimum cost paths, application examples. Dijkstra's algorithm and A* algorithm, conditions for A* to be optimal.
° Sensor based path planning: Bug1, Bug2 and Tangent Bug algorithms.
° Potential field based algorithms.
° Road map based algorithms: visibility graph, Generalized Voronoï Diagrams, sensor based construction of the GVD.
° Sampling based algorithms: Probabilistic Road Maps, road map construction algorithms, local planner.
- Enseignant: Franco FUSCO
- Enseignant: Olivier Kermorgant