Real-time collision detection is one of the most important intelligent processings in robotics. In collision detection, a large storage capasity is usually required to store the 3-dimensional information on the obstacles located in a workspace. Moreover, high-computational power is essential in not only coordinate transformation but also matching operation. In the proposed collision detection VLSI processor, the matching operation is drastically accelerated by using a content-addressable memory (CAM). A new obstacle representation based on a union of rectangular solids is also used to reduce the obstacle memory capacity, so that the collision detection can be performed by only magnitude comparison in parallel. Parallel architecture using several identical processor elements (PEs) is employed to perform the coordinate transformation at high speed, and each PE performs coordinate transformation at high speed based on the COordinate Rotation DIgital Computation (CORDIC) algorithms. When the 16 PEs and 144-kb CAM are used, the performance is evaluated to be 90 ms.

This paper presents a new high-speed three-dimensional (3-D) object recognition system based on two-dimensional (2-D) chain code matching. An observed 3-D object is precisely represented by a 2-D chain code sequence from the discrete surface points of the 3-D object, so that any complex objects can be recognized precisely. Moreover, the normalization procedures such as translation, rotation of 3-D objects except scale changes can be performed systematically and regularly regardless of the complexity of the shape of 3-D objects, because almost all the normalization procedures of 3-D objects are included in the 2-D chain code matching procedure. As a result, the additional normalization procedure become only the processing time for scale changes which can be performed easily by normalizing the length of the chain code sequence. In addition, the fast fourier transformation (FFT) is applicable to 2-D chain code matching which calculates cross correlation between an input object and a reference model, so that very fast recognition is performed. In fact, it is demonstrated that the total recognition time of a 3-D ofject is estimated at 5.35 (sec) using the 28.5-MIPS SPARC workstation.