Another example of sensorless manipulation will now be described, which was developed by Goldberg and Mason in [394,395,396]; see also . A Java implementation of the algorithm appears in . Suppose that convex, polygonal parts arrive individually along a conveyor belt in a factory. They are to be used in an assembly operation and need to be placed into a given orientation. Figure 12.50 shows a top view of a parallel-jaw gripper. The robot can perform a squeeze operation by bringing the jaws together. Figure 12.50a shows the part before squeezing, and Figure 12.50b shows it afterward. A simple model is assumed for the mechanics. The jaws move at constant velocity toward each other, and it is assumed that they move slowly enough so that dynamics can be neglected. To help slide the part into place, one of the jaws may be considered as a frictionless contact (this is a real device; see ). The robot can perform a squeeze operation at any orientation in (actually, only is needed due to symmetry). Let denote the set of all squeezing actions. Each squeezing action terminates on its own after the part can be squeezed no further (without crushing the part).
The planning problem can be modeled as a game against nature. The initial orientation, , of the part is chosen by nature and is unknown. The state space is . For a given part, the task is to design a sequence,
Consider how a part in an unknown orientation behaves. Due to rotational symmetry, it will be convenient to describe the effect of a squeeze operation based on the relative angle between the part and the robot. Therefore, let , assuming arithmetic modulo . Initially, may assume any value in . It turns out that after one squeeze, is always forced into one of a finite number of values. This can be explained by representing the diameter function , which indicates the maximum thickness that can be obtained by taking a slice of the part at orientation . Figure 12.51 shows the slice for a rectangle. The local minima of the distance function indicate orientations at which the part will stabilize as shown in Figure 12.50b. As the part changes its orientation during the squeeze operation, the value changes in a way that gradually decreases . Thus, can be divided into regions of attraction, as shown in Figure 12.52. These behave much like the funnels in Section 8.5.1.
The critical observation to solve the problem without sensors is that with each squeeze the uncertainty can grow no worse, and is usually reduced. Assume is fixed. For the state transition equation , the same will be produced for an interval of values for . Due to rotational symmetry, it is best to express this in terms of . Let denote relative orientation obtained after a squeeze. Since is a function of and , this can be expressed as a squeeze function, , defined as
Any interval can be interpreted as a nondeterministic I-state, based on the history of squeezes that have been performed. It is defined, however, with respect to relative orientations, instead of the original states. The algorithms discussed in Section 12.1.2 can be applied to . A backward search algorithm is given in  that starts with a singleton, nondeterministic I-state. The planning proceeds by performing a backward search on . In each iteration, the interval, , of possible relative orientations increases until eventually all of is reached (or the period of symmetry, if symmetries exist).
The algorithm is greedy in the sense that it attempts to force to be as large as possible in every step. Note from Figure 12.52 that the regions of attraction are maximal at the minima of the diameter function. Therefore, only the minima values are worth considering as choices for . Let denote the preimage of the function . In the first step, the algorithm finds the for which is largest (in terms of length in ). Let denote this relative orientation, and let . For each subsequent iteration, let denote the largest interval in that satisfies
Suppose that the sequence has been computed. This must be transformed into a plan that is expressed in terms of a fixed coordinate frame for the robot. The -step action sequence is recovered from
Steven M LaValle 2020-08-14