A method for robot obstacle avoidance and path planning is proposed. The algorithm is based on a camera image feedback loop utilizing a neural network for image processing. The method can successfully generate collision-free paths in a 2D robot workspace containing randomly-placed polygonal obstacles. The control algorithm is simple and robust and has low computational requirements. Controller simulation implemented on a personal computer can generate collision-free robot paths in real time, requiring approximately 1 sec. per robot move.>

An automatic wafer pattern inspection system has been developed that can detect defective patterns 6 mu m or larger in multilayered wafer patterns at a speed 30 times faster than that of a human inspector. The false-alarm rate is less than 0.5 occurrences/chip. This performance is achieved mainly by the use of a special comparison method between two adjacent patterns obtained through a single optical setup, and also by the use of digital design pattern data (CAD data). The main functions of the design pattern data are to specify the inspection area, to designate optimum parameters for inspection, and to separate defective portions into different layers, thereby facilitating the classification of the defects. All image processing is performed in one pass by a high-speed pipeline-structured image processor that can analyze an input image signal at a 7 MHz video rate.>

A fully automatic transistor wire-bonding system utilizing image-processing techniques is described. The system is composed of a minicomputer, image processors, and a maximum of 50 wire-bonding machines. Each machine has a TV camera installed to pick up, through a microscope, the image of transistor chips fed into the machine. In this system, one image processor services several machines in a group and analyzes the image signals in a time-sharing mode to determine the chip position at each machine. From the detected coordinates, an XY servomechanism in each machine is activated to bond and stretch wires between the electrodes on the chip and the outer leads. The recognition of electrode positions is accomplished by the multiple local pattern matching method in which redundancy is used in achieving a high recognition rate. An analysis of the system and basic features of the position recognition algorithm are also described. This method opens the possibility of economical automation of processes possible only by the human eye to date.

An intelligent robot that recognizes and assembles three-dimensional objects by means of vidicon cameras, an articulated mechanical hand, and a digital computer is described. Its problem-solving functions include three essential parts: the recognition of macro-instructions from a human master, the recognition of the objects to be handled, and the decision making for executing the necessary tasks. The instruction is in the form of a three-view plan of a simple polyhedral assemblage whose overall spatial configuration is recognized together with its component parts. In this process the set of planes of the assemblage is disassembled into open shells; these are then reconstructed into closed shells to find the parts by solving a linear equation where the shell vectors are taken into consideration. In the object recognition, all the geometric features of polygonal prisms in the field of vision are extracted to find the specific parts required for the assembly. Further computation is made to search for the assembly procedure and is based on the restraint vectors of each object and consequent disassembly free. Finally, the mechanical hand starts the autonomous manipulation of the parts so as to accomplish the assigned assembly.