The need for dextrous manipulation arises when the physical dimensions and mechanical properties of the materials to be handled may not be known precisely. For such applications the gripper should be able to control the position of its finger or the force which it exerts on the object, and also be able to detect slip and take corrective action. This paper describes certain aspects of design involved and experiments carried out using a dextrous gripper which is being developed for nuclear applications.

A walking machine must plan its footsteps in such a way that it is able to follow the desired route without stepping over the bad patches on the ground. A good way of doing this is to search through a graph of possible moves. We have previously done that for a four-legged machine (1990, 1991). For a six-legged machine, however, there are too many ways of choosing a stable support pattern. So, strong heuristics are needed to reduce the search. In this paper, a novel heuristic rule, based on the support state transitions of the wave gait, has been presented. This rule helps in converging to and maintaining the wave gait through a limited search, while retaining adequate options to deviate from the wave gait, as and when needed, to adapt to the terrain, or to execute a generalised motion. Thus, we have a gait generation mechanism that combines the efficiency of the wave gait with the terrain adaptivity of the free gait.>

Redundant manipulators have more degrees of freedom than what is absolutely necessary for performing a task. The extra degrees of freedom can be used for avoiding obstacles or to optimize certain performance indices like manipulability or task compatibility. Maximizing manipulability keeps the manipulator away from singularities and provides more velocity transmission ratios in all directions. Optimizing task compatibility improves the force/velocity transmission ratios in the specified directions. However, the real time implementation of various optimizing algorithms is difficult because of the need of large computing time. In the present work, robot configurations for an optimum performance index are computed throughout the workspace. These configurations are then used to train a layered feed forward neural network (FFNN). During operation of the robot, the trained neural net outputs optimal configurations in real-time. The neural net captures the gross behaviour of the training data rather than memorizing the individual data, as in a lookup table. Thus its output is smooth and ideally suited for control purposes. We have simulated this approach on a 3-DOF redundant planar manipulator and the results are discussed in this paper.>

A method is presented for the generation of a free gait for the straight-line motion of a quadruped walking machine. It uses a heuristic graph search procedure based on the A* algorithm. The method essentially looks into the consequences of a move to a certain depth before actually committing to it. Deadlocks and inefficiencies are thus sensed well in advance and avoided.>

A method is presented for the generation of a locally optimal free gait for 2-D generalized motion of a quadruped walking machine. It employs a heuristic graph search procedure based on the A* algorithm. The method essentially looks into the consequences of a move to a certain depth before actually committing to it. Deadlocks and inefficiencies are thus sensed well in advance and avoided.>