Real-time on-the-fly Motion planning via updating tree data of RRT* using Neural network inference

In this study, we consider the problem of motion planning for urban air mobility applications to generate minimal snap trajectory and trajectory that cost minimal time to reach goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach We propose dis a decoupled method that includes designing a policy network based on recurrent neural network for the reinforcement learning algorithm, and then combine an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using generative adversarial imitation learning algorithm for training recurrent neural network based policy network and generating time optimized trajectory. Simulation results show that our approaches have short computation time when compared to other algorithms with similar performance while guaranteeing sufficien tex ploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles and re-planned trajectories maintain continuity for executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling to perform on-the-fly update of final landing position, and to optimize path and trajectory in real-time while keeping explorations in the environment.