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Several descriptions of redundancy are presented in the literature, often from widely different perspectives. Therefore, a discussion of these various definitions and the salient points would be appropriate. In particular, any definition and redundancy needs to cover the following issues; the difference between multiple solutions and an infinite number of solutions; degenerate solutions to inverse kinematics; task redundancy; and the distinction between non-redundant, redundant and highly redundant manipulators.

A task based approach to the issue of redundant robots starts from the premise that there are obstacles that cannot be removed from the working area and which therefore must be avoided. This statement produces the requirement for a device with a certain degree of mobility, and stresses the need to ensure that the aim is twofold: reach the goal and avoid obstacles. But avoiding obstacles is not the same objective as keeping as far away from an obstacle as possible; the primary goal is still to reach the target. In fact humans use soft contact to reach targets that are at the periphery of their reach. This soft distributed contact has the effect of smoothing the surface of the object and hence there is an element of only being interested in obstacle detail at the appropriate scale to achieve the task. This paper describes a new approach to collision avoidance based on using a global path finding algorithm, in this case using Laplacian potential fields, in conjunction with a simple local geometrically based algorithm for avoiding obstacles and maximising the use of manoeuvring space in a manner which is not limited by digital computation resolution issues. This extra technique is in some ways analogous to the human soft contact approach. Three examples are presented to illustrate the robustness of the algorithm. In order to be able to compare results with other techniques, an environment measurement scheme is defined which gives an indication of the difficulty of the trajectory being followed.