When John von Neumann gave his lecture on ‘General and Logical Theory of Automata’ in 1948 his ideas were met with some scepticism. The suggestion that life is a logical process which could result in making a new kind of creature, was the forerunner of all our discussions on whether ‘artificial life’ was at all possible.

This paper describes a hierarchical architecture for rhythmic movement generation, which suits a juggling-like task involving sensory-motor coordination. Our approach, which is interpreted as a “bidirectional weak coupling” to the environment, does not require a continuous monitoring of the environment, but can adapt a robot to a change in the environment, owing to the interaction between the robot and the environment at the ball contact. The proposed architecture contains two passive-control mechanisms, the “entrainment mechanism” and the “open-loop stable mechanism,” that lead to the emergence of a self-organized temporal order in the whole system. This dynamic temporal pattern enables a robot to perform a stable rhythmic movement. We demonstrate a robot which juggles two balls rebounding off the wall and confirm the effectiveness of our approach.

The Director of the Social Ecology Laboratory at the Universite Libre de Bruxelles has described the success of a 3 million European Union (EU) experiment for pest control. Dr. Jean-Louis Deneubourg believes that the EU funded initiative has many ramifications for more than just pest control.

Industrial robots in poorly structured environments have to interact compliantly with this environment for successful operations. In this paper, we present a behaviour-based approach to learn peg-in-hole operations from scratch. The robot learns autonomously the initial mapping between contact states to motion commands employing fuzzy rules and creating an Acquired-Primitive Knowledge Base (ACQ-PKB), which is later used and refined on-line by a Fuzzy ARTMAP neural network-based controller. The effectiveness of the approach is tested comparing the compliant motion behaviour using the ACQ-PKB and a priori Given-Primitive Knowledge Base (GVN-PKB). Results using a KUKA KR15 industrial robot validate the approach.

This paper reports on the control structure of the pneumatic biped Lucy. The robot is actuated with pleated pneumatic artificial muscles, which have interesting characteristics that can be exploited for legged locomotion. They have a high power to weight ratio, an adaptable compliance and they can absorb impact effects.

The discussion of the control architecture focuses on the joint trajectory generator and the joint trajectory tracking controller. The trajectory generator calculates trajectories represented by polynomials based on objective locomotion parameters, which are average forward speed, step length, step height and intermediate foot lift. The joint trajectory tracking controller is divided in three parts: a computed torque module, a delta-p unit and a bang-bang pressure controller. The control design is formulated for the single support and double support phase, where specifically the trajectory generator and the computed torque differs for these two phases.

The first results of the incorporation of this control architecture in the real biped Lucy are given. Several essential graphs, such as pressure courses, are discussed and the effectiveness of the proposed algorithm is shown by the small deviations between desired and actual attained objective locomotion parameters.

It is with much regret that I've to resign from the editorship of Robotica after 23 years of service because of ill-health and advanced age. I hope the new editor Greg Chirikjian will make further progress in expanding the scope of the journal and responding to the challenges of our times.

Organic transistors have been developed to a state where, combined with pressure sensors, they may prepare the way for the development of an artificial skin that could be utilised by robots and in automation applications.

Teleoperated robots are used to perform tasks that human operators cannot carry out because of the nature of the tasks themselves or the hostile nature of the working environment. Though many control architectures have been defined for developing these kinds of systems reusing common components, none has attained all its objectives because of the high variability of system behaviors. This paper presents a new architectural approach that takes into account the latest advances in robotic architectures while adopting a component-oriented approach. This approach provides a common framework for developing robotized systems with very different behaviors and for integrating intelligent components. The architecture is currently being used, tested and improved in the development of a family of teleoperated robots which perform cleaning of ship-hull surfaces.

Robot collision characteristics are analyzed by using the idea of the collision map. This analysis consists of the translations of the collision region on the collision map and they correspond to parallel movements of the original robot path. These translations are investigated in several cases and applied to general situations in which two robots are moving or working in a common workspace. Also, the collision characteristics are analyzed for a few special situations where the analysis of collision characteristics is crucial and the resultant solution for collision avoidance can be obtained.

This paper proposes a stable control structure for the bilateral teleoperation of mobile robots. The proposed control structure includes a time-delay compensation placed on both the local and remote sites of the teleoperation system. To illustrate the performance and stability of the proposed control structure, experiences on a Pioneer 2DX mobile robot teleoperated through a commercial joystick with visual feedback, are shown.

A novel composite control scheme for a manipulator with flexible links and joints is presented that uses the singular perturbation technique (SPT) to divide the manipulator dynamics into reduced order slow and fast subsystems. A neural network controller is then applied for the slow subsystem and a state-feedback H∞ controller for the fast subsystem. Results are presented that demonstrate improved performance over an alternative SPT-based controller that uses inverse dynamics and LQR controllers.

This paper addresses the path planning problem for manipulators. The problem of path planning in robotics can be defined as follows: To find a collision free trajectory from an initial configuration to a goal configuration. In this paper a collision-free path planner for manipulators, based on a local constraints method, is proposed. In this approach the task is described by a minimization problem under geometric constraints. The anti-collision constraints are mapped as linear constraints in the configuration space and they are not included in the function to minimize. Also, the task to achieve is defined as a combination of two displacements. The first displacement brings the robot towards to the goal configuration, while the second one allows the robot to avoid the local minima. This formulation solves many of classical problems found in local methods. However, when the robot acts in some heavy cluttered environments, a zig-zaging phenomenon could appear. To solve this situation, a graph based on the local environment of the robot is constructed. On this graph, an A* search is performed, in order to find a dead-lock free position that can be used as a sub-goal in the optimization process. This path-planner has been implemented within SMAR, a CAD-Robotics system developed at our laboratory. Tests in heavy cluttered environments were successfully performed.

This paper introduces a three degree of freedom XYZ Micromanipulator (XYZM) that is fabricated in the $x$-$y$ plane and positions components in the $x$, $y$, and $z$ directions using three independent linear inputs. The mechanism positions components on a platform using three legs, each composed of a slider mechanism and a parallelogram mechanism.

Three versions of the XYZM were fabricated and tested using surface micromachining processes: the rigid-body, offset, and compliant XYZM. Slider displacements of 45 micrometers result in a predicted out-of-plane displacement of 188 micrometers for the rigid-body XYZM, 205 micrometers for the offset XYZM, and 262 micrometers for the compliant XYZM.

In this paper, we have developed and implemented a system that combines autonomous obstacle avoidance with force-reflective tele-operation. In this system, a tele-operated mobile robot is controlled by a local two-degrees-of-freedom force-reflective joystick that a human operator holds while he is monitoring the screen. The force-reflective joystick transforms the relation between a mobile robot and the environment to the operator as a virtual force. A virtual force is generated in the form of a new collision vector and reflected to the operator, which makes the tele-operation of a mobile robot safe from collision in an uncertain and obstacle-cluttered remote environment. A mobile robot controlled by a local operator usually takes pictures of remote environments and sends the images back to the operator over the Internet. Because of limitations of communication bandwidth and the narrow view-angles of the camera, the operator cannot observe shadow regions and curved spaces. To overcome this problem, a new form of virtual force is generated along the collision vector according to both distance and approaching velocity between an obstacle and the mobile robot, which is obtained from ultrasonic sensors. This virtual force is transferred back to a master (two degrees of freedom joystick) over the Internet to enable a human operator to feel the geometrical relation between the mobile robot and the obstacle. It is demonstrated by experiments that this haptic reflection improves the performance of a tele-operated mobile robot significantly.

This paper presents a study of the application of adaptive and robust control methods to a cooperative manipulation system which is developed for handling an object by three dimensional revolute-jointed manipulators. The adaptive control algorithm supports the parameter adaptive law that provides guaranteed stability for uncertain systems. In designing the robust control structure, contact and friction constraints for grasp and bearing conditions, structural flexibility or such similar factors as various unmodeled dynamics are considered as uncertainties that determine available values of control parameters. The novelty of results in the present paper is to define new control inputs using parametric uncertainties and the Lyapunov based theory of guaranteed stability of uncertain systems for handling objects in a spatial workspace.

This paper presents studies of the coordination of human upper body voluntary movement. A minimum-jerk 3D model is used to obtain the desired path in Cartesian space, which is widely used in the prediction of human reach movement. Instead of inverse kinematics, a direct optimization approach is used to predict each joint's profile (a spline curve). This optimization problem has four cost function terms: (1) Joint displacement function that evaluates displacement of each joint away from its neutral position; (2) Inconsistency function, which is the joint rate change (first derivative) and predicted overall trend from the initial target point to the final target point; (3) The non-smoothness function of the trajectory, which is the second derivative of the joint trajectory; (4) The non-continuity function, which consists of the amplitudes of joint angle rates at the initial and final target points, in order to emphasize smooth starting and ending conditions. This direct optimization technique can be used for potentially any number of degrees of freedom (DOF) system and it reduces the cost associated with certain inverse kinematics approaches for resolving joint profiles. This paper presents a high redundant upper-body modeling with 15 DOFs. Illustrative examples are presented and an interface is set up to visualize the results.

In this paper, a 3-DOF parallel kinematic machine (PKM) with a passive link is introduced. The forward kinemaic model is established, and a new technique is proposed to solve this model. The developed forward kinematic solver (FKS) is employed in two new applications: the determination of joint workspace and sensor-based real-time monitoring. A joint workspace concept is proposed for the optimization of a PKM structure. It is defined as a reachable area in the joint coordinate system under given ranges of active joint motions. The larger a joint workspace is, the higher utilization of joint motion capacity is. Sensor-based monitoring is applied in real-time system operation, by which a remote user can monitor a PKM through Internet based on the feedbacks of the joint encoders or the on-site stereo camera.

In this paper, a collision detection and identification method of a manipulator, using wrist and base force/torque sensors, is presented. An impact model is used to simulate the interaction between the manipulator and the human or environment. A neural network approach and a model based method are developed to detect the collision forces and disturbance torques on the joints of the manipulator. The experimental results illustrate the validity of the developed collision detection and identification scheme.

This paper proposes a new visual positioning method for a humanoid robot to approach and grasp a valve based on colour and shape constraints. The robot has two cameras in its head and uses constraints of colour rectangle marks to determine the valve's position and pose. When the hands are near the valve, an image-based visual servoing method is employed to catch the handle of the valve via cameras in end-effectors. Experimental results are presented to verify the effectiveness of the proposed method.

In this paper a discrete-time observer-based approach to Fault Detection and Isolation (FDI) for industrial robotic manipulators is presented and experimentally tested. In order to counteract the effects of unmodeled dynamics and disturbances, a time-delayed estimate of such effects is adopted. Remarkably, the observer is designed directly in the discrete-time domain. The performance of the proposed approach are experimentally verified on a six-degrees-of-freedom industrial robot.