Data-Driven Motion Planning : With Application for Heavy Duty Vehicles

Abstract: Motion planning consists of finding a feasible path of an object between an initial state and a goal state, and commonly constitutes a sub-system of a larger autonomous system. Motion planners that utilize sampling-based algorithms create an implicit representation of the search space via sampling said search space. Autonomous systems that rely on real-time motion planning benefit from the ability of these algorithms to quickly compute paths that are optimal or near optimal. For sampling-based motion planning algorithms, the sampling strategy greatly affects the convergence speed of finding these paths, i.e., how the sampling distribution is shaped within the search space. In baseline approaches, the samples may be drawn with uniform probability over this space. This thesis project explores a learning-based approach that can utilize experience from previous successful motion plans to provide useful information in novel planning scenarios, as a means of improvement over conventional motion planning methods. Specifically, the focus has been on learning the sampling distributions in both the state space and the control space of an autonomous ground vehicle. The innovatory parts of this work consist of (i) learning the control space sampling distributions, and (ii) learning said distributions for a tractor-trailer system. At the core of the method is an artificial neural network consisting of a conditional variational autoencoder. This artificial neural network is capable of learning suitable sampling distributions in both the state space and control space of a vehicle in different planning scenarios. The method is tested in four different environments and for two kinds of vehicles. Evaluation is partly done by comparison of results with a conventional motion planning algorithm. These evaluations indicates that the artificial neural network can produce valuable information in novel planning scenarios. Future work, primarily on how the artificial neural network may be applied to motion planning algorithms, is necessary to draw further conclusions.