Design and Development of an Integrated Mobile Robot System for Use in Simple Formations

In recent years, formation control of autonomous unmanned vehicles has become an active area of research with its many broad applications in areas such as transportation and surveillance. The work presented in this thesis involves the design and implementation of small unmanned ground vehicles to be used in leader-follower formations. This mechatronics project involves breadth in areas of mechanical, electrical, and computer engineering design. A vehicle with a unicycle-type drive mechanism is designed in 3D CAD software and manufactured using 3D printing capabilities. The vehicle is then modeled using the unicycle kinematic equations of motion and simulated in MATLAB/Simulink. Simple motion tasks are then performed onboard the vehicle utilizing the vehicle model via software, and leader-follower formations are implemented with multiple vehicles

Real-time trajectory generation for dynamic systems with nonholonomic constraints using Player/Stage and NTG.

This thesis will present various methods of trajectory generation for various types of mobile robots. Then it will progress to evaluating Robot Operating Systems (ROS’s) that can be used to control and simulate mobile robots, and it will explain why Player/Stage was chosen as the ROS for this thesis. It will then discuss Nonlinear Trajectory Generation as the main method for producing a path for mobile robots with dynamic and kinematic constraints. Finally, it will combine Player, Stage, and NTG into a system that produces a trajectory in real-time for a mobile robot and simulates a differential drive robot being driven from the initial state to the goal state in the presence of obstacles. Experiments will include the following: Blobfinding for physical and simulated camera systems, position control of physical and simulated differential drive robots, wall following using simulated range sensors, trajectory generation for omnidirectional and differential drive robots, and a combination of blobfinding, position control, and trajectory generation. Each experiment was a success, to varying degrees. The culmination of the thesis will present a real-time trajectory generation and position control method for a differential drive robot in the presence of obstacles

Adaptive Multi-Vehicle Area Coverage Optimization System and Method

A mission planning system for determining an optimum use of a plurality of vehicles in searching a predefined geographic area (PGA). A discretizer subsystem may be used for sensing the capabilities of each vehicle to produce a point set defining a number of points within the PGA that the vehicles must traverse to completely search the PGA. A task allocator subsystem may determine an optimum division of the PGA into different subregions to be handled by specific ones of the vehicles, thus to minimize an overall time needed to search the PGA. A path optimizer subsystem may determine an optimum path through a particular vehicle\u27s assigned subregion to minimize the time needed for each specific vehicle to traverse its associated subregion

Integration of fault tolerance and hardware redundancy techniques into the design of mobile platforms

This work addresses the development of a fault-tolerant mobile platform. Fault-tolerant mechanical system design is an emerging technology that attempts to build highly reliable systems by incorporating hardware and software architectures. For this purpose, previous work in fault-tolerant were reviewed. Alternate architectures were evaluated to maximize the fault tolerance capabilities of the driving and steering systems of a mobile platform. The literature review showed that most of the research work on fault tolerance has been done in the area of kinematics and control systems of robotic arms. Therefore, hardware redundancy and fault tolerance in mobile robots is an area to be researched. The prototype constructed as part of this work demonstrated basic principles and uses of a fault-tolerant mechanism, and is believed to be the first such system in its class. It is recommended that different driving and steering architectures, and the fault-tolerant controllers\u27 performance be tested on this prototype

An intelligent, free-flying robot

The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base

Collaborator: A Nonholonomic Multiagent Team for Tasks in a Dynamic Environment

In our previous work, we proposed a potential field-based hybrid path planning scheme for robot navigation that achieves complete coverage in various tasks. This paper is an extension of this work producing a multiagent framework, Collaborator, that integrates a high-level negotiation-based task allocation protocol with a low-level path planning method taking into consideration several real-world robot limitations such as nonholonomic constraints. Specifically, the proposed framework focuses on a class of complex motion planning problems in which robots need to cover the whole workspace, coordinate the accomplishment of a task, and dynamically change their roles to best fit the task. Applications in this class of problems include bomb detection and removal as well as rescuing of survivors from accidents or disasters. We have tested the framework in simulations of several tasks and have shown that Collaborator can satisfy nonholonomic constraints, cooperatively accomplish given tasks in an initially unknown dynamic environment while avoiding collision with other team members. Finally we prove that the proposed control laws are stable using the Lyapunov stability theory

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation

Trajectory Servoing: Image-Based Trajectory Tracking without Absolute Positioning

The thesis describes an image based visual servoing (IBVS) system for a non-holonomic robot to achieve good trajectory following without real-time robot pose information and without a known visual map of the environment. We call it trajectory servoing. The critical component is a feature based, indirect SLAM method to provide a pool of available features with estimated depth and covariance, so that they may be propagated forward in time to generate image feature trajectories with uncertainty information for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than SLAM pose-based feedback and further improved by a weighted least square controller using covariance from the underlying SLAM system.M.S

A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms