Realtime motion path generation using subtargets in a rapidly changing environment

In this work an algorithm is proposed for path planning in a rapidly changing environment. The algorithm is computationally cheap and generates a sub-optimal smooth path with bounds on the allowed velocity, acceleration, and jerk. The algorithm is designed for holonomic omniwheel platforms. It outperforms potential field algorithms regarding both convergence and optimality. Furthermore, it is able to adapt fast in a rapidly changing environment due to the low computational cost in the order of ms for a single update, in contrast with computationally more expensive methods such as wavefront algorithms and global optimization methods, where the computational cost is mostly on the order of seconds. The algorithm will be tested via simulations and experiments.