J. De Schutter

Optimal robot excitation and identification

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in the case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and acceleration in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.

Compliant robot motion II. A control approach based on external control loops

A control approach for the execution of robot tasks in contact with the environment is worked out. The input to the con troller consists of the task specification described in part I. The control approach is based on external force and tracking loops, which are closed around the robot positioning system. The position control loops tend to decouple and linearize the complex robot dynamics, and therefore they present to the external controllers a system which is easy to model and easy to control. Design and properties of external control loops are discussed in great detail. In particular, the role of a passive compliance with respect to task execution speed and distur bance rejection is analyzed both qualitatively and quantita tively. The resulting compliant motion controller has been tested experimentally, and proved to be very robust and to yield the theoretically expected performance.A control approach for the execution of robot tasks in contact with the environment is worked out. The input to the con troller consists of the task specification described in part I. The control approach is based on external force and tracking loops, which are closed around the robot positioning system. The position control loops tend to decouple and linearize the complex robot dynamics, and therefore they present to the external controllers a system which is easy to model and easy to control. Design and properties of external control loops are discussed in great detail. In particular, the role of a passive compliance with respect to task execution speed and distur bance rejection is analyzed both qualitatively and quantita tively. The resulting compliant motion controller has been tested experimentally, and proved to be very robust and to yield the theoretically expected performance.

Time-Optimal Path Tracking for Robots: A Convex Optimization Approach

This paper focuses on time-optimal path tracking, a subproblem in time-optimal motion planning of robot systems. Through a nonlinear change of variables, the time-optimal path tracking problem is transformed here into a convex optimal control problem with a single state. Various convexity-preserving extension are introduced, resulting in a versatile approach for optimal path tracking. A direct transcription method is presented that reduces finding the globally optimal trajectory to solving a second-order cone program using robust numerical algorithms that are freely available. Validation against known examples and application to a more complex example illustrate the versatility and practicality of the new method.

Compliant robot motion: I. A formalism for specifying compliant motion tasks

A formalism is developed for specifying compliant motion tasks. It is based on the hybrid control functional specification method described by Mason. However, some new concepts are introduced: tracking directions, end-effector and task- frame motion constraints, feedforward velocity data, and task termination conditions. This formalism synthesizes all the information required in order to allow a completely auto matic execution of the task. As a result, it achieves strict separation between programming and control, which is of primary importance for the integration of compliant motion into a robot programming language. Several examples show that the formalism applies to a broad class of compliant motion tasks. The newly defined tracking directions contrib ute to the autonomy of the robot control system in case only partial geometric information about the environment is avail able.A formalism is developed for specifying compliant motion tasks. It is based on the hybrid control functional specification method described by Mason. However, some new concepts are introduced: trac...

Specification of force-controlled actions in the "task frame formalism"-a synthesis

Autonomous robot tasks involving contacts with the environment must be performed under active force control if the geometric uncertainties in the task models are too large to cope with by means of passive compliance only. In practice, task specification of force-controlled actions is closely linked to the task frame formalism (TFF), also known as the compliance frame formalism. The TFF is a very intuitive and controller-independent approach to model a motion constraint, and to specify the desired forces and motions compatible with this constraint. However, it has never been defined clearly and unambiguously, and it cannot cope with all possible constrained motion tasks. This paper provides, for the first time, a formal definition of what makes up a TFF task specification. It gives also a synthesis of which tasks the TFF can cope with, and proposes a generic textual task specification formalism. Finally, it describes an example constrained motion task that the TFF cannot handle.

A smoothly constrained Kalman filter

This paper presents the Smoothly Constrained Kalman Filter (SCKF) for nonlinear constraints. A constraint is any relation that exists between the state variables. Constraints can be treated as perfect observations. But, linearization errors can prevent the estimate from converging to the true value. Therefore, the SCKF iteratively applies nonlinear constraints as nearly perfect observations, or, equivalently, weakened constraints. Integration of new measurements is interlaced with these iterations, which reduces linearization errors and, hence, improves convergence compared to other iterative methods. The weakening is achieved by artificially increasing the variance of the nonlinear constraint. The paper explains how to choose the weakening values, and when to start and stop the iterative application of the constraint.

Virtual decomposition based control for generalized high dimensional robotic systems with complicated structure

This paper presents a systematic adaptive control strategy which can accomplish a variety of control objectives (position control, internal force control, constraints,and optimizations) for the generalized high-dimensional robotic systems (GHDRS) without restriction on target systems. Based on the concept of virtual decomposition by which a GHDRS is virtually decomposed into several objects and base-floating open chains, the motion control problem of the original system is converted into that of each object and that of each open chain, individually, while the internal force control as well as the constraint force control may be performed with respect to each object only. This feature makes it possible to implement the control algorithm of each subsystem in modularly structured hardware which can be integrated to form any specific robot controller dedicated to a specific application. In the sense of Lyapunov, it is declared that the dynamic coupling between every two physically connected subsystems can be completely represented by the so-called virtual power flows (VPFs) at the cutting points between them. Asymptotic stability of the complete system can be ensured by choosing the system Lyapunov function as the sum of all nonnegative accompanying functions assigned for the subsystems. Some possible applications based on the proposed approach are discussed. Finally, computer simulations of two PUMA 560 arms transporting a common object along a prespecified trajectory are carried out to verify the stability and robustness issues of the system.

Human-inspired robot assistant for fast point-to-point movements

A first step towards truly versatile robot assistants consists of building up experience with simple tasks such as the cooperative manipulation of objects. This paper extends the state-of-the-art by developing an assistant which actively cooperates during the point-to-point transportation of an object. Besides using admittance control to react to interaction forces generated by its operator, the robot estimates the intended human motion and uses this identified motion to move along with the operator. The offered level of assistance can be scaled, which is vital to give the operator the opportunity to gradually learn how to interact with the system. Experiments revealed that, while the robot is programmed to adapt to the human motion, the operator also adapts to the offered assistance. When using the robot assistant the required forces to move the load are greatly reduced and the operators report that the assistance feels comfortable and natural.

Hybrid vision/force control at corners in planar robotic-contour following

The accuracy and execution speed of a force controlled contour-following task is limited if the shape of the workpiece is unknown. This is even more true when the workpiece contour contains corners. The paper shows how a hybrid vision/force control approach at corners in planar-contour following results in a more accurate and faster task execution. The vision system is used to measure online the contour and to watch out for corners. The edge is correctly located by compensating the compliance of the tool/camera setup which affects the contour measurement. A simple corner-detection algorithm is presented. Once a corner is detected, the finite-state controller is activated to take the corner in the best conditions. Experimental results are presented to validate the approach.

A self-learning automaton with variable resolution for high precision assembly by industrial robots

This paper reports on the use of the stochastic automaton theory to configure control algorithms for high precision assembly operations performed with a force-sensing robot. The basic principle of the stochastic automation, i.e., its variable structure, has been extended to the dimensionality of the automaton by gradually optimizing the resolution of the input variables.