Robert H. Cannon

Experiments on the Control of a Satellite Manipulator

Automation is becoming increasingly important to the exploration and utilization of space. Space-based robotic systems will provide efficient and inexpensive means to work in space. The dynamic control of space robots presents unique challenges, partly due to the robot’s lack of a fixed base.

Experiments with a dual-armed, cooperative, flexible-drivetrain robot system

The design and experimental performance of a two-armed robot are presented. The manipulators have added drivetrain flexibility to aid studying their effects on robot cooperation. The control system is a four-level hierarchy: joint, arm, object, and task levels. The joint level handles flexibility via joint-torque control. The arm level uses nonlinear end-point feedback to control tip forces and positions. The object level manages the object via object impedance control. The task level directs multistep tasks autonomously. Experiments are shown for each level, culminating with two-handed insertion of a long part into a deep hole.<<ETX>>

Experimental results of two free-flying robots capturing and manipulating a free-flying object

This paper presents the results of laboratory experiments performed at the Aerospace Robotics Laboratory (ARL) at Stanford University from 1987 to 1993 that successfully demonstrate a team, of two-armed free-flying robots capturing, transporting, and docking a large, freely moving object. In these experiments, the object and robots float on a thin cushion of air over a granite surface plate, simulating with high fidelity in two dimensions the drag-free, zero-gravity conditions of space. A human user indicates a desired object location and orientation through a graphical user interface. The self-propelled robots then capture and so position the object, with no additional input required from the user: the human is at the task-defining level. On command, the robot team docks the captured object with a stationary second object. The paper discusses the experimental facility, the control hierarchy that supports object-based task-level control, and the controllers for the object and robots. Experimental results are then presented for the capture, transportation, and docking of the object.

Proximate time-optimal algorithm for on-line path parameterization and modification

This paper presents an new, proximate-optimal solution to the path-constrained time-parameterization problem. This new algorithm has three distinguishing features: First, the run-time worst-case complexity of the proximate time-optimal algorithm is linear with respect to path-length and it is shown to be more efficient than any other truly time-optimal algorithm. Second, for a given robotic system, the algorithms running-time is predictable as a function of the length of the path (allowing its use in combination with time-aware planners). Third, the algorithm easily supports the modification of on-going trajectories. The algorithm has been extensively tested and is operational in a number of robotic systems including a dual-arm workcell, an underwater robotic system, and the Marsokhod Rover vehicle. Experimental results presented illustrate the online use of the algorithm with a path planner to allow capture and delivery of objects from a moving conveyor belt.

System design and interfaces for intelligent manufacturing workcell

This paper introduces a design technique for complex robotic systems called interfaces-first design. Interfaces-first design develops information interfaces based on the characteristics of information flow in the system, and then builds subsystem interfaces from combinations of these information interfaces. This technique is applied to a dual-arm workcell combining a graphical user interface, an on-line motion planner, real-time vision, and an on-line simulator. The system is capable of performing object acquisition from a moving conveyor belt and carrying out simple assemblies, without the benefit of pre-planned schedules nor mechanical fixturing. The information characteristics of this system are analyzed, and divided into three interfaces: world state, task command, and motion commands. Detailed descriptions of the resulting interfaces are provided. The paper concludes with experimental results from the workcell. Both single-arm and dual-arm actions are discussed.

Experiments in nonlinear adaptive control of multi-manipulator free-flying robots

This paper gives an overview of the nonlinear adaptive control work that was completed at the Stanford University Aerospace Robotics Laboratory (ARL) in December 1992. A new task-space adaptive control framework was developed that is able to provide continuously full adaptation capability to complex robot systems in all modes of operation. This framework consists of an inverse-dynamics adaptation algorithm that has been generalized beyond simple joint or endpoint control, a new system modelling technique to simplify the generation of a system model to ease greatly the implementation of the adaptive control algorithm, and the development of the task-space concept to allow operators to specify a robots task, which can include payload positions, endpoint positions, and joint configurations as subsets. The task-space adaptive control framework has been experimentally demonstrated on the ARL Multi-Manipulator, Free-Flying Space Robot performing capture and manipulation of free-floating objects with unknown inertial properties-without requiring human assistance.<<ETX>>

Symbolic dynamic modelling and analysis of object/robot-team systems with experiments

This paper presents an approach for the symbolic dynamic modelling of object/robot-team systems composed of an object manipulated by a team of r robots. The modelling approach merges the dynamic models of the object and robots into a system model. Derivations show that the system acceleration can be computed with complexity proportional to r. This paper demonstrates in a detailed example with physical experiments how the modelling approach can be used for symbolic analysis of a closed-loop object/robot-team system.

Utilizing human vision and computer vision to direct a robot in a semi-structured environment via task-level commands

A novel approach to directing a highly autonomous robot operating in a semi-structured environment is presented. In this approach, the human operator assists the robot in perceiving unexpected situations in the environment through simple point-and-click type interaction with a live video display from cameras on-board the robot. As a result of this high-level guidance, the robot is now able to invoke a variety of computer vision algorithms to augment the world model accordingly. This novel approach utilizes the complimentary vision capabilities of both the human and computer to extend the capability of the human/robot team to overcome the challenges of semi-structured environments without sacrificing the high-degree of autonomy and resilience to time delay of the task-level command architecture. Preliminary experimental results with a laboratory robot are presented.

A decentralized object impedance controller for object/robot-team systems: theory and experiments

This paper derives a decentralized object impedance controller (DOIC) especially suited for use by a team of robots and/or multiple manipulators. In contrast to the original object impedance controller (OIC), a separate DOIC operates on each robot to provide local computation of the manipulator force commands, thus removing the need for expensive high-bandwidth communication of force signals between the robots. The key advance of the DOIC that provides this benefit is a decentralized algorithm for estimating the external force on the object. This paper derives the DOIC, provides stability analysis, and verifies the theory with definitive physical experiments.