Gabriele M. T. D'Eleuterio

Parallel O(log N) algorithms for the computation of manipulator forward dynamics

In this paper, two parallel O(log N) algorithms for the computation of manipulator forward dynamics are presented. They are based on a new O(N) algorithm for the problem which is developed from a new factorization of mass matrix M. Specifically, a factorization of the inverse M/sup -1/ in the form of a Schur complement is derived. The new O(N) algorithm is then developed as a recursive implementation of this factorization. It is shown that the resulting algorithm is strictly parallel, that is, it is less efficient than other algorithms for serial computation of the problem. However, to our knowledge, it is the only algorithm that can be parallelized to derive both a time-optimal O(logN) - and processor-optimal - O(N) - parallel algorithm for the problem. A more efficient parallel O(logN) algorithm based on a multilevel exploitation of parallelism is also briefly described. In addition to their theoretical significance, these parallel algorithms allow a practical implementation due to their simple architectural requirements. >

Experiments in end-effector tracking control for structurally flexible space manipulators

An experimental study of a control policy for end-effector trajectory tracking of structurally flexible space-based manipulators is presented. The controller employs a fully feedback-driven approach using a series of steady-state linear regulators. An augmented dynamical description involving derivatives of the control inputs is employed to ensure smooth force and/or torque profiles at the a joints. Experiments were performed on Radius, a two-link planar manipulator with flexible links and rotary joints supported on a horizontal table by air pucks. Radius is designed so that its frequencies of vibration are comparable to those of space manipulators. The arm is instrumented with potentiometers for measuring joint angles and rates as well as strain gages for monitoring link deformation. The joints are actuated by DC motors coupled to harmonic drive gear reducers. Experimental results show that the controller is able to track demanding end-effector trajectories very well. These results agree closely with computer simulations. >

Kinematic path-planning for formations of mobile robots with a nonholonomic constraint

A method of planning paths for formations of mobile robots with nonholonomic constraints is presented. The kinematics equations presented in this paper allow a general geometrical formation of mobile robots to be maintained while the group as a whole travels an arbitrary path. It is possible to represent a formation of mobile robots by a single entity with the same type of nonholonomic constraint as the individual members. Thus, any path-planner or control method may be used with the formation as would be applied to an individual robot. Equations are developed for changing the geometrical formation and hardware results are presented from the Stanford MARS Testbed.

CMAC adaptive control of flexible-joint robots using backstepping with tuning functions

A neural network used in a direct-adaptive control scheme can achieve trajectory tracking of a (highly) flexible joint robot holding an unknown payload without need for many learning repetitions. A modification of the Lyapunov stable nonlinear control method known as backstepping with tuning functions is derived to achieve this. Specifically, the introduction of appropriate weightings of the different tuning-function terms results in high performance. Also, a robust redesign of the tuning function method is presented to account for the uniform approximation (modeling) error of the neural network. This computationally burdensome method is made practical by taking advantage of the efficient structure of the CMAC neural network. Simulations with a (highly) flexible-joint robot show immediate compensation for a payload with performance nearly recovered after five seconds.

An evolutionary approach to multiagent heap formation

An approach to evolving globally coordinated behaviours in groups of autonomous mobile robots is presented. The control system in each robot is identical and consists of a cellular automaton which serves to arbitrate between a number of fixed basis behaviours. Genetic algorithms search for cellular automata whose arbitration results in success on a predefined task. Heap formation is presented as an example of a task requiring global coordination. Simulation results are provided.

Tracking control for end‐effector position and orientation of structurally flexible manipulators

A simple and effective feedback control strategy is presented for end-effector position and orientation tracking of structurally flexible manipulators free of external forces as in space applications. The fully feedback-driven approach employs an augmented dynamical description in which derivatives of the control inputs are included in the state. This ensures smooth control inputs to the manipulator joints. The feedback law uses gain scheduling of a series of steady-state regulators derived by considering the manipulator at intermediate (nominally rigid and stationary) configurations along the desired trajectory. The performance of the control method is demonstrated in simulations of a planar three-link manipulator system. Examples show that the controller can be applied successfully in discrete-time, and that spillover does not appear to be a problem.

A Neuroevolutionary Approach to Emergent Task Decomposition

A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, more modular network architectures that exploit emergent task decomposition strategies can evolve faster and outperform comparably smaller non emergent neural networks for this task. Much like biological nervous systems, larger Emergent Task Decomposition Networks appear to evolve faster than comparable smaller networks. Unlike reinforcement learning techniques, only a global fitness function is specified, requiring limited supervision, and self-organized task decomposition is achieved through competition and specialization. The results are derived from computer simulations.

The "feature CMAC": a neural-network-based vision system for robotic control

A strategy for locating and grasping a target object in an unknown position using a robotic manipulator equipped with a CCD camera is described. Low-level trajectory and joint control during the grasping operation is handled by the manipulators conventional motion controller using target-pose data provided by an artificial-neural-network-based vision system. The feature CMAC is a self-organizing neural network that efficiently transforms images of a target into estimates of its location and orientation. The approach emulates biological systems in that it begins with simple image features (e.g., corners) and successively combines them to form more complex features in order to determine object position. Knowledge of camera parameters, camera position and object models is not required since that information is incorporated into the network during a training procedure wherein the target is viewed in a series of known poses. The manipulator is used to generate the training images autonomously. No training of connection weights is required; instead, training serves only to define the network topology which requires just one pass through the training images. Experiments validating the effectiveness of the strategy on an industrial robotic workcell are presented.

Subsurface surveying by a rover equipped with ground-penetrating radar

We discuss our experiences in integrating a commercial off-the-shelf ground-penetrating radar unit with an all-terrain rover. Straight-line subsurface surveys were generated in a fully autonomous manner using odometry and a simple visual servoing technique. Survey results for various terrains are presented. We discuss the configuration of the integrated system and make recommendations for both Martian and terrestrial applications.

Computer simulation of elastic chains using a recursive formulation

A computer simulation procedure for the dynamics of topological chains, using a recursive Newton-Euler formulation, is presented. The bodies of the chain are, in general, elastic and the joints can permit arbitrary (rotational and/or translational) interbody motion. Relative interbody translation, however, is assumed small. As an example, a three-link quasianthropomorphic flexible-link manipulator is studied. The simulation results underscore the importance of modeling structural flexibility.<<ETX>>