Lazy Steering RRT*: An Optimal Constrained Kinodynamic Neural Network Based Planner with no InExploration Steering KinodynamicRRT* provides a sampling based asymptoticallyoptimal solution for motion planning of kinematically and dynamicallyconstrained robots. For nonlinear systems, normally, the time and energyclamped steering function solutions needed within the RRT* use numerical iterative schemes such as shooting methods, which are computationally cumbersome. The number of calls to these solvers increases with the size of the tree. Hence, the time complexity of finding feasible steering functions prevents kinodynamicRRT* for nonlinear systems from being utilized in realtime or in situations where fast planning and replanning are needed. Kinematicdynamic constraints reduction to make the steering functions solvable in real time has been proposed in literature, however, these methods would affect the optimality of the solution. In this paper, we propose a lazysteering kinodynmaic RRT* in which, machine learning techniques are used to (1) predict if a randomlyselected node is steerable to, and (2) if the steering is deemed feasible, what would be the estimated energy cost associated, when executing it. This provides a promising framework for implementing KinodynamicRRT* in which the use of numerical methods is delayed (hence the name lazy steering) until a potential collision free path has been found, and only then the numerical techniques are invoked. This results in a huge improvement in the run time with little trade off on optimality. Our proposed method was tested via simulation for motion planning of an underactuated, nonholonomic, quadcopter with nonlinear dynamics in an environment cluttered with obstacles. The lazysteering RRT* was faster than its counterpart (which was based on some recent works) by two orders of magnitude.

Fast Motion Planning via Free Cspace Estimation Based on Deep Neural Network This paper presents a novel learningbased method for fast motion planning in highdimensional spaces. A deep neural network is designed to predict the free configuration space rapidly given the environment point cloud. With a generated roadmap as an approximate view of the free Cspace, LazyPRM is applied to find and check the path with A* search. Due to the application of LazyPRM, the presented method can preserve probabilistic completeness and asymptotic optimality. The new algorithm is tested on a 3DOF robot arm and a 6DOF UR3 robot to plan in randomly generated obstacle environments. Results indicate that compared to planners including PRM, RRT*, RRTconnect and the original LazyPRM, our method is of the lowest time consumption and relatively short path length, showing good performance on both planning speed and path quality.