Lidar-based exploration and discretization for mobile robot planning
This work provides a method for mobile robots to bridge the gap between continuous sensor data and discrete planning, which is an incremental improvement for robotic navigation.
This paper addresses the problem of bridging low-level control and high-level planning for robotic systems by proposing a discretization algorithm that identifies free polytopes from lidar point cloud data. It constructs a transition graph where nodes are free polytopes and edges represent intersections, with an associated distance measure for transition quality. The free polytopes encode the environment for collision-free trajectory planning.
In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete description. This paper considers the problem of bridging the low-level control and high-level planning for robotic systems via sensor data. In particular, we propose a discretization algorithm that identifies free polytopes via lidar point cloud data. A transition graph is then constructed where each node corresponds to a free polytope and two nodes are connected with an edge if the two corresponding free polytopes intersect. Furthermore, a distance measure is associated with each edge, which allows for the assessment of quality (or cost) of the transition for high-level planning. For the low-level control, the free polytopes act as a convenient encoding of the environment and allow for the planning of collision-free trajectories that realizes the high-level plan. The results are demonstrated in high-fidelity ROS simulations and experiments with a drone and a Segway.