GOP - Generating optimal paths for industrial and humanoid robots in complex environments
The generation of the best possible path that does not violate any constraints imposed by the environment is an ubiquitous task in both industrial and humanoid robotics. Currently there is no algorithmic approach available that allows to address this problem for very complex dynamic robot systems in cluttered changing environments in real time. Instead there are two established but still quite separated research areas that both address a part of the problem, namely path planning and numerical optimal control. Path planning is mainly interested in the determination of a feasible path in very complex environments based on geometric and kinematic models. Numerical optimal control techniques are capable to generate optimal trajectories for robot manipulators or humanoid robots taking into account the dynamics; however the treatment of a huge number of environmental constraints giving rise to many local minima makes the problems very hard, if not impossible, to solve. This project aims at combining state of the art developments of these two domains and to create the algorithmic foundations to tackle real time optimal control problems in cluttered environments.