For the purpose of guiding a movable robot on a wide and flat floor like a usual factory, a gymnasium etc., we propose a specially designed guiding sign. It consists of a cylinder on whose surface four slits are cut in, and a fluorescent light which is placed just on the axis of the cylinder. Two of the slits are parallel to each other, and the other two are angled. A robot takes an image of the sign with a TV camera. After the thresholding operation, we have four bright sets of pixels which correspond to the four slits of the cylinder. By measuring the relative distances between those four points, we can compute the distance and the angle to the direction of the sign using simple geometrical equations. We have built an actual sign. Using a personal computer with an image processing capability, we have investigated accuracy of the proposed position identification method, and compared the experimental results against the theoretical analysis of measurement error. The data shows good coincidence between the analysis and the experiments. Finally, we have built a movable robot, which has three microprocessors and a TV camera, and done several trajectory following control experiments.
The Driving Pipeline is a driving control scheme for a mobile robot that drives the robot vehicle outdoors continuously and adaptively. Although the basic idea of the Driving Pipeline originates from a pipelined computer architecture, the Driving Pipeline adopts more complex execution management for adaptive vehicle motion. Like the pipelined computer architecture, the Driving Pipeline segments necessary computation for robot vehicle motion into several successive subprocesses and executes them on the pipelined processing modules that operate in parallel. Because of this pipelined architecture, the Driving Pipeline offers high computation performance, and then vehicle's high speed and continuous motion. Unlike the pipelined computer architecture, however, the Driving Pipeline adjusts execution cycles in order to adapt vehicle motion both to driving environment and computation resources in robot systems. For adaptive control, the Driving Pipeline introduces control parametes and defines required relations among them. Because of the explicit control scheme, the Driving Pipeline not only enables adaptive driving control but also analyzes the robot navigation. The Driving Pipeline illustrates mid level navigation between the driving control and the high level map navigation. Introducing this navigation layer offers more adaptability to the environment.
It is necessary for an autonomous mobile robot (AMR) to describe schemes of monitoring the status and procedures of suitable processes. This paper presents a sensor-based navigation method using fuzzy control, of which purpose is to construct an expert knowledge for efficient and better piloting of AMR. This method provides a function of tracing a planned path by sensing distanes of AMR and its difference angle from the planned path, and a function of avoiding stationary and moving-obstacles by sensing free area distances ahead of AMR. Fuzzy control is also used to select a suitable-rules (tracing a path/avoiding obstacles) according to a situation, which is derived from sensor information by using fuzzy control. The effectiveness of established rules and the effect of fuzzy control 'to an AMR navigation is discussed through simulations.
This study treats a particular control problem of an inverted pendulum with flexible structure. An inverted pendulum problem is a fundamental of stabilizing unstable systems such as a walking robot. Furthermore, consideration of the flexibility is essential for control of a light weight mechanical system with quick motion. A controlled system in this study consists of a wire-driven carrier, a flexible beam hinged to the carrier and a weight fixed at the other side of the beam. Linear quadratic control was applied to stabilize the system using a personal computer. However, steady vibration due to the coulomb friction was observed in the system. Characteristics of the nonlinear vibration were well explained by the analysis using the method of averaging. A simple recursive algorithm was employed to estimate the value of the Coulomb friction. With an on-line estimation and compensation of the Coulomb friction, linear quadratic control was successfully applied to stabilize, the system.
The purpose of this research is to make a jumping machine jump over a big obstacle and a ditch. Therefore, the authors plan to do fundamental researches to (1) make jumping machine jump as highly as possible, (2) control the direction of the jumping, (3) control the attitude of it in the air and (4) make it land softly. In this paper the way to make the jumping machine land softly is described. The optimum softlanding of the jumping machine is defined as follows: •First, the impulse which a jumping machine receives from outside is minimized. •Secondly, the maximum of the force which the jumping machine receives from outside is minizized. When the jumping machine lands, we apply this definition to the body and the leg of the jumping machine. Then the validity of the softlanding is proved by the experiments usining the jumping machine.
Dancing robots are a kind of performance robots. It is not only to be able to dance but also to be able to make spectartors feel familiar and mild. For this purpose, this dancing robot was manufactured after consideration factors of subjective judgement. Image design technique was adopted for familiar shape and SMA actuator was adopted for smooth action of arms and legs. It was investigated that spectators' image for this dancing robot with Semantic Defferential Technique what was one of subjective judgement technique in psychology. This dancing robot's achivement was finally evaluated with a respect to its familality. As a conclusion of this development, it was indecated that Semantic Defferential Technique gave basis of psycological engineering design of performance robots.
This paper proposes a tangent graph, which is defined on the basis of a new concept“local shortest path”, for path planning of a point robot in environments, where there exist not only polygonal obstacles but also curved obstacles. The local shortest path is defined as a path which is the shortest in its neighboring region, and on the basis of this concept a collision-free path can be planned by selecting common tangents of the obstacles. In the tangent graph, a node corresponds to a tangent point on obstacle boundaries, and an edge represents a collision-free common tangent of obstacles or a boundary segment between two tangent points on the same boundary. The. tangent graph has the same data structure with the visibility graph but it has less edges than its corresponding visibility graph. When the number of convex segments of obstacle boundaries is denoted by K, the tangent graph requires O (K2) memory for an environment with curved obstacles. For a polygonal environment, the size of the data structure is O (M2+N) , where M and N denote the numbers of convex components and convex vertices of the obstacles. The tangent graph can be used to plan a collision-free path not only among polygonal obstacles but also among curved obstacles, whereas the visibility graph is limited to a polygonal environment.
In this paper, we discuss a matching method of objects from range data measured more sparsely than the size of object's local structure. In such a case, observed data differ from models in several reasons; 1) occlusion of hollow part, 2) omission of feature elements, 3) bluntness of edges, 4) isolation of observed points, and 5) inconsistency with general knowledges. The aim of the paper is to develop a way to compare sparse imcomplete data with complete model description in hierarchical manner using strip tree, and the validity of the algorithm is demonstrated for 2-D examples. We also discuss 3-D matching of depth images by extending strip tree.