Quantized feedback control of autonomous robots

In this dissertation, a new robot navigation framework is introduced to close thegap between the environment mapping, path planning computations and the vehicle control problem. General approaches for robot navigation deals with the path planning and navigation with the assumption that the control system of the robot can trackthe given reference values of the planners. On the other hand, the control system architecture assumes that the incoming reference signals are suitable for its control range or commonly thresholds the out of range signals. In both situations, these systems act separately from each other, and there is no backwards information passed through these systems. As a result, even the path and motion planner outputs looks like optimal outcomes, if these outputs do not have any compliance with the control system, then the total system behavior is not accomplished as expected.To ensure connection with the planning and control structures and establish a feedback mechanism to the overall system, navigation framework employs the quantization operator both in the environment mapping and in the controller architecture. Using the quantizer in grid segmentation problem leads to a novel approach within occupancy grid literature. By using computer graphics methodologies, an efficient occupancy grid method is derived for assigning the point clouds that are gathered from 3D sensors to the grids. This assignment uses a hybrid approach, which combines the topological and grid space partitioning methods. Geometric properties of the grid voxels are interpreted as quantized spatial information from the environment and used as an input to the motion planner that generates reference signals to the controller side of the robotics system.The first main part of the navigation framework is called as input quantizer. Sensor inputs and the path splines are represented with the independent graph nodes for each axis in the 3D Euclidean space. Space partitioning methodology is executed with a binary segmentation independent for each axis until the corresponding node of the path splines and the obstacle nodes have no intersection. This binary segmentation and its reverse operation are called as zoom-in and zoom-out properties respectively in the study. After the motion planner that shapes the reference signals with using the information from the grid voxels, another main block of the framework is in charge of synthesizing an asymptotically stable controller, which is called output quantizer. As its name suggests, this structure also consists of a quantizer, which is denominated as qLQR.This method is adapted to the proposed framework to satisfy both the stability and the performance concerns.