An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation

We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

Omnidirectional Control of the Hexapod Robot TigerBug

TigerBug is a six legged, hexapod robot built and designed by students in the Rochester Institute of Technology\u27s (RIT) Multi Agent Bio-Robotics Laboratory (MABL). TigerBug is comprised of 18 servo motors, 3 degrees of freedom (DOF) per leg, supported by carbon fiber wrapped foam legs placed in a circular pattern around its hexagon shaped body. In order to control such a complex system, much research has been done in the field of kinematics. There exist two derivations of kinematic solutions, forward and inverse. The forward kinematic (FK) solution tends to be much simpler than its inverse kinematic (IK) counterpart. There has been many methods developed to quickly, and efficiently solve the IK in order to control the position and orientation of a robot. This thesis details the process of developing the IK solution and two gait algorithms for TigerBug. The IK solution was developed by first solving for the FK solution of TigerBug using Denavit-Hartenberg (DH) Parameters. After the FK solution was solved, differentials were applied to each equation in order to solve for the IK solution. Once the IK solution was tested, a fixed gait algorithm was developed in order to understand basic motion control of hexapod locomotion. Once the fixed gait was implemented successfully a rule-based free gait algorithm was developed. The rule-based free gait was accomplished using the rule set governed by restrictiveness to determine when leg state transitions were to occur, as described in the literature. Once implemented, the different combinations of gait parameters were tested for quickness of convergence and efficiency to determine the most optimal set of walking parameters for TigerBug

The power dissipation method and kinematic reducibility of multiple-model robotic systems

This paper develops a formal connection between the power dissipation method (PDM) and Lagrangian mechanics, with specific application to robotic systems. Such a connection is necessary for understanding how some of the successes in motion planning and stabilization for smooth kinematic robotic systems can be extended to systems with frictional interactions and overconstrained systems. We establish this connection using the idea of a multiple-model system, and then show that multiple-model systems arise naturally in a number of instances, including those arising in cases traditionally addressed using the PDM. We then give necessary and sufficient conditions for a dynamic multiple-model system to be reducible to a kinematic multiple-model system. We use this result to show that solutions to the PDM are actually kinematic reductions of solutions to the Euler-Lagrange equations. We are particularly motivated by mechanical systems undergoing multiple intermittent frictional contacts, such as distributed manipulators, overconstrained wheeled vehicles, and objects that are manipulated by grasping or pushing. Examples illustrate how these results can provide insight into the analysis and control of physical systems

Testing of FTS fingers and interface using a passive compliant robot manipulator

This report deals with testing of a pair of robot fingers designed for the Flight Telerobotic Servicer (FTS) to grasp a cylinder type of Orbital Replaceable Unit (ORU) interface. The report first describes the objectives of the study and then the testbed consisting of a Stewart Platform-based manipulator equipped with a passive compliant platform which also serves as a force/torque sensor. Kinematic analysis is then performed to provide a closed-form solution for the force inverse kinematics and iterative solution for the force forward kinematics using the Newton's Raphson Method. Mathematical expressions are then derived to compute force/torques applied to the FTS fingers during the mating/demating with the interface. The report then presents the three parts of the experimental study on the feasibility and characteristics of the fingers. The first part obtains data of forces applied by the fingers to the interface under various misalignments, the second part determines the maximum allowable capture angles for mating, and the third part processes and interprets the obtained force/torque data

Robot graphic simulation testbed

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts

Aspects of an open architecture robot controller and its integration with a stereo vision sensor.

The work presented in this thesis attempts to improve the performance of industrial robot systems in a flexible manufacturing environment by addressing a number of issues related to external sensory feedback and sensor integration, robot kinematic positioning accuracy, and robot dynamic control performance. To provide a powerful control algorithm environment and the support for external sensor integration, a transputer based open architecture robot controller is developed. It features high computational power, user accessibility at various robot control levels and external sensor integration capability. Additionally, an on-line trajectory adaptation scheme is devised and implemented in the open architecture robot controller, enabling a real-time trajectory alteration of robot motion to be achieved in response to external sensory feedback. An in depth discussion is presented on integrating a stereo vision sensor with the robot controller to perform external sensor guided robot operations. Key issues for such a vision based robot system are precise synchronisation between the vision system and the robot controller, and correct target position prediction to counteract the inherent time delay in image processing. These were successfully addressed in a demonstrator system based on a Puma robot. Efforts have also been made to improve the Puma robot kinematic and dynamic performance. A simple, effective, on-line algorithm is developed for solving the inverse kinematics problem of a calibrated industrial robot to improve robot positioning accuracy. On the dynamic control aspect, a robust adaptive robot tracking control algorithm is derived that has an improved performance compared to a conventional PID controller as well as exhibiting relatively modest computational complexity. Experiments have been carried out to validate the open architecture robot controller and demonstrate the performance of the inverse kinematics algorithm, the adaptive servo control algorithm, and the on-line trajectory generation. By integrating the open architecture robot controller with a stereo vision sensor system, robot visual guidance has been achieved with experimental results showing that the integrated system is capable of detecting, tracking and intercepting random objects moving in 3D trajectory at a velocity up to 40mm/s