° 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.