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.

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.

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 algorithm's 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.

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.

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.

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.

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 robot's 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.>

The dynamic control module of the Dynamic and Strategic Control of Cooperating Manipulators (DASCCOM) project for aerospace robotics is presented. The cooperative manipulation problem is analyzed from a systems perspective, and the desirable features of a control system for cooperative manipulation are discussed. A control policy is developed that enforces a controlled impedance not of the individual arm endpoints, but of the manipulated object itself. A parallel implementation for a multiprocessor system is presented. The controller fully compensates for the system dynamics and directly controls the object internal forces. Most importantly, it presents a simple, powerful, intuitive interface to higher level strategic control modules. Experimental results from a dual two-link-arm robotic system show the effectiveness of the object impedance controller compared to other strategies, both for free-motion slews and environmental contact.>

The authors present the dynamic control module of the Dynamic and Strategic Control of Cooperative Manipulators project at Stanford University's Aerospace Robotics Laboratory. First, the cooperative manipulation problem is analyzed from a systems perspective, and the desirable features of a control system for cooperative manipulation are discussed. Next, a control policy is developed that enforces a controlled impedance not of the individual arm endpoints, but of the manipulated object itself. A parallel implementation for a multiprocessor system is presented. The controller fully compensates for the system dynamics and directly controls the object internal forces. Most importantly, it presents a simple, powerful, intuitive interface to the strategic controller. Experimental results for a dual two-link arm robotic system are presented to verify the controllers performance, for both free-motion slews and environmental contact.>