This paper addresses a method of satisfactorily controlling the grasp of objects. Emphasis is placed on achieving the desired stiffness of a grasped object as accurately as possible, especially when the fingers have redundant joints. A model describing the relation between stiffness and force is derived. Based upon this model, a hierarchical control scheme of the grasp stiffness, called decentralized object stiffness control (DOSC) is proposed. DOSC is composed of a fingertip stiffness synthesis (FSS) algorithm and orthogonal stiffness decomposition control (OSDC). Employing the proposed FSS always achieves the desired grasp stiffness by solving the constrained least square problem. The computed fingertip stiffness is achieved by OSDC. It offers a feasible way of controlling the fingertip stiffness as well as maintaining the stability of the finger configuration by modulating the joint stiffness. The developed control method is implemented on a two-fingered planar robot hand one finger of which has a redundant joint. The effectiveness of the control method is confirmed experimentally.

The use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks.

Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while the overall aim is devoted to the community of cooperative Agents. These Agents, which our testbed concerns, include Vision, Planner, World Model and the Robot itself.

The heavy reliance which New Zealand places on its primary industries makes it important that every effort be made to automate these industries to improve their productivity. This presents many unique problems, especially when consideration is given to the use of robots in the horticultural sector for the handling of soft fruit for export. As part of this process, fruit must be individually packed in trays without the risk of damage. This is currently achieved through labour-intensive manual handling and for this reason a range of specialised robot grippers have been developed. These, to varying degrees, overcome many of the problems associated with the harsh handling which occurs when conventional industrial robot grippers are used.

This paper presents a general technique to model flexible components (mainly links and joints flexibilities are considered) of manipulator arms based on Castigliano's theorem of least work. The robotic arms flexibility properties are derived and represented by the matrix of compliance coefficients. Such expressions can be used to determine the errors due to the robotic tip deformations under the application of a set of applied loads at the tip in a Cartesian space. Once these deformations are computed, they may be used to correct for the positional errors arisen from the robotic structural deformations in the motion control algorithms.

The coupled dynamic response of two cooperating robots handling two flexible payloads is treated using a new algorithm. In this algorithm, the dynamic equations describing the system are obtained using Lagrange's method for the rigid robot links and the finite element method for the flexible payloads. The contact between the flexible payloads is modelled using the penalty function method and a contact search algorithm is employed to identify the contact region.

A parameter optimization approach to the time-minimization of robotic motions along specified paths is presented for the case when: (i) the velocity profile is a prescribed sequence of constant acceleration/deceleration segments with unspecified, but bounded vertex velocities at given path stations; (ii) the relative robot/path location can be varied. Such optimizations occur when technological requirements impose a certain velocity profile along the path due to velocity and acceleration constraints. Full nonlinear manipulator dynamics and path parameterization are used to determine the optimal velocity profile and robot location consistent with the actuator/configuration limitations. No numerical integration or search for switching curve are involved in the solution. Examples of time-and-location optimized robotic motions with specified velocity profile are presented.

This paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.