Optimal Motion Planning with constraints for mobile robot navigation

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 34-36).Motion planning is the process of planning a sequence of motions to move an object from one configuration to another. Recently, randomized techniques known as PRMs have shown great potential for solving motion planning problems in complicated high-dimensional space. Motion Planning, or path planning for robots, becomes increasing difficult as the dimension of the planning space increases with the robot's degrees of freedom (dof). While the running time of deterministic motion planning algorithms grows exponentially with an increase in dof, PRMs can produce solutions in times that do not depend on the dof but only the difficulty of the problem. PRMs randomly generate collision free configurations in a robot's Configuration-space (Cspace), representing feasible positions and orientations for the robot. Nearby configurations are linked together by so-called local planners, and these connections are edges in a roadmap, a graph containing representative discrete paths the robot may travel. We present methods to extract optimal paths from roadmap-based motion planners. Our system uses Markov-like states and flexible goal states so that general optimization criteria including collision detection, kinematic/dynamic constraints, or minimum clearance can be used in various applications. Our algorithm is an augmented version of Dijkstra's shortest path algorithm. We present simulation results maximizing minimum path clearance, minimizing localization effort, and enforcing kinematic/ dynamic constraints

Extracting optimal paths from roadmaps for motion planning

We present methods for extracting optimal paths from motion planning roadmaps. Our system enables any combination of optimization criteria, such as collision detection, kinematic/dynamic constraints, or minimum clearance, and relaxed definitions of the goal state, to be used when selecting paths from roadmaps. Our algorithm is an augmented version of Dijkstra’s shortest path algorithm which allows edge weights to be defined relative to the current path. We present simulation results maximizing minimum path clearance, minimizing localization effort, and enforcing kinematic/dynamic constraints. I

A framework for roadmap-based navigation and sector-based localization of mobile robots

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator

Energy efficient path planning and model checking for long endurance unmanned surface vehicles.

In this dissertation, path following, path planning, collision avoidance and model checking algorithms were developed and simulated for improving the level of autonomy for Unmanned Surface Vehicle (USV). Firstly, four path following algorithms, namely, Carrot Chasing, Nonlinear Guidance Law, Pure pursuit and LOS, and Vector Field algorithms, were compared in simulation and Carrot Chasing was tested in Unmanned Safety Marine Operations Over The Horizon (USMOOTH) project. Secondly, three path planning algorithms, including Voronoi-Visibility shortest path planning, Voronoi-Visibility energy efficient path planning and Genetic Algorithm based energy efficient path planning algorithms, are presented. Voronoi-Visibility shortest path planning algorithm was proposed by integrating Voronoi diagram, Dijkstra’s algorithm and Visibility graph. The path quality and computational efficiency were demonstrated through comparing with Voronoi algorithms. Moreover, the proposed algorithm ensured USV safety by keeping the USV at a configurable clearance distance from the coastlines. Voronoi-Visibility energy efficient path planning algorithm was proposed by taking sea current data into account. To address the problem of time-varying sea current, Genetic Algorithm was integrated with Voronoi-Visibility energy efficient path planning algorithm. The energy efficiency of Voronoi-Visibility and Genetic Algorithm based algorithms were demonstrated in simulated missions. Moreover, collision avoidance algorithm was proposed and validated in single and multiple intruders scenarios. Finally, the feasibility of using model checking for USV decision-making systems verification was demonstrated in three USV mission scenarios. In the final scenario, a multi-agent system, including two USVs, an Unmanned Aerial Vehicle (UAV), a Ground Control Station (GCS) and a wireless mesh network, were modelled using Kripke modelling algorithm. The modelled uncertainties include communication loss, collision risk, fault event and energy states. Three desirable properties, including safety, maximum endurance, and fault tolerance, were expressed using Computational Tree Logic (CTL), which were verified using Model Checker for Multi-Agent System (MCMAS). The verification results were used to retrospect and improve the design of the decision-making system.PhD in Aerospac