The majority of independent locomotion microrobots pack batteries as their energy source. However, because the energy that can be stored in a battery is proportional to its volume, the operating time becomes shorter as the robot becomes smaller. To solve this problem the energy must be supplied from outside by wireless transmission. We propose a microwave energy transmission system for microrobots in metal piping. Because microwave is rectified and converted in the form of electric energy in this system, we developed a receiving antenna for microrobots in piping and a microwave rectifying circuit to generate high voltage. These were loaded on a microrobot, tested to drive a locomotive mechanism, and the efficiency of the proposed system was confirmed.

In this paper, we propose the distributed stable marriage problem and apply it to planning for cooperative works of autonomous mobile robots and battery charger stations. We develop and analyze a distributed algorithm to determine the partner by message communication.

In this study, an extraction method of failure sound signal which is strongly contaminated by noise is investigated by genetic algorithm and statistical tests of the frequency domain for the failure diagnosis of machinery. In order to check the extraction accuracy of the failure signal and obtain the optimum extraction of failure signal, the "existing probability Ps (t*k) of failure signal" and statistical information Iqp are defined as the standard indices for evaluation of the extraction results. It has been proven by practical field data and application of the inspection and diagnosis robot that the extraction method discussed in this paper is effective for detection of a failure and distinction of it's origin in the diagnosis of machinery.

In this paper, we present a vision system for a depalletizing robot which recognizes carton objects. The algorithm consists of the extraction of object candidates and a labeling process to determine whether or not they actually exist. We consider this labeling a combinatorial optimization of labels, we propose a new labeling method applying Genetic Algorithm (GA). GA is an effective optimization method, but it has been inapplicable to real industrial systems because of its processing time and difficulty of finding the global optimum solution. We have solved these problems by using the following guidelines for designing GA: (1) encoding high-level information to chromosomes, such as the existence of object candidates; (2) proposing effective coding method and genetic operations based on the building block hypothesis; and (3) preparing a support procedure in the vision system for compensating for the mis-recognition caused by the pseudo optimum solution in labeling. Here, the hypothesis says that a better solution can be generated by combining parts of good solutions. In our problem, it is expected that a global desirable image interpretation can be obtained by combining subimages interpreted consistently. Through real image experiments, we have proven that the reliability of the vision system we have proposed is more than 98% and the recognition speed is 5 seconds/image, which is practical enough for the real-time robot task.

In case of object recognition using 3-D configuration data, the scale and poses of the object are important factors. If they are not known, we can not compare the object with the models in the database. Hence we propose a strategy for object recognition independently of its scale and poses, which is based on Hopfield neural network. And we also propose a strategy for estimation of the camera motion to reconstruct 3-D configuration of the object. In this strategy, the camera motion is estimated only with the sequential images taken by a moving camera. Consequently, the 3-D configuration of the object is reconstructed only with the sequential images. And we adopt the multiple regression analysis for estimation of the camera motion parameters so as to reduce the errors of them.

We study robot navigation in unknown environment with rectangular obstacles aligned with the x and y axes. We propose a strategy called the modified-bian heuristic, and analyze its efficiency. Let n be the distance between the start point and the target of robot navigation, and let k be the maximum side length among the obstacles in a scene. We show that if k=(o(n) and if the summation of the widths of the obstacles on the line crossing the target and along the y axis is o(n), then ratio of the total distance walked by the robot to the shortest path length between the start point and the target is at most arbitrarily close to 1+k/2, as n grows. For the same restrictions as above on the sizes of the obstacles, the ratio is also at most arbitrarily close to 1+3/4n, as n grows, where is the summation of lengths of the obstacles in y axis direction.

An approach to estimate the information of moving objects is described in terms of their kinetic and static properties such as 2D velocity, acceleration, position, and the size of each object for the features of motion snd shape. To obtain the information of motion/shape of multiple objects, an advanced contour matching scheme is developed, which includes the synthesis of edge images and the analysis of object shape with a high matching confidence as well as a low computation cost. The scheme is composed of three algorithms: a motion estimation by an iterative triple cross-correlation, an image synthesis by shifting and masking the object, and a shape analysis for determining the object size. Implementing fuzzy membership functions to the object shape, the scheme gets improved in accuracy of capturing motion and shape of multiple moving objects. Experimental result shows that the proposed method is valid for several walking men in real scene.

We consider a class of unknown scenes Sk(n) with rectangular obstacles aligned with the axes such that Euclidean distance between the start point and the target is n, and any side length of each obstacle is at most k. We propose a strategy called the adaptive-bias heuristic for navigating a robot in such a scene, and analyze its efficiency. We show that a ratio of the total distance walked by a robot using the strategy to the shortest path distance between the start point and the target is at most 1+(3/5) k, if k=o(n) and if the start point and the target are at the same horizontal level. This ratio is better than a ratio obtained by any strategy previously known in the class of scenes, Sk(n), such that k=o(n).

This paper describes an adaptive sensing system with tracking and zooming a moving object in the stable environment. Both the close contour matching technique and the effective determination of zoom ratio by fuzzy control are proposed for achieving the sensing system. First, the estimation of object feature parameters, 2-dimensional velocity and size, is based on close contour matching. The correspondence problem is solved with cross-correlation in projections extracted from object contours in the specialized difference images. In the stable environment, these contours matching, capable of eliminating occluded contours or random noises as well as background, works well without heavy-cost optical flow calculation. Next, in order to zoom the tracked object in accordance with the state of its shape or movement practically, fuzzy control is approached first. Three sets of input membership function--the confidence of object shape, the variance of object velocity, and the object size--are evaluated with the simplified implementation. The optimal focal length is achieved of not only desired size but safe tracking in combination with fuzzy rule matrix constituted of membership functions. Experimental results show that the proposed system is robust and valid for numerous kind of moving object in real scene with system period 1.85 sec.

In the applications of the fast Fourier transform (FFT) to real-world computation such as robot vision, high-speed processing with small latency is an important issue. In this paper, we propose a linear array processor for the minimum-latency FFT computation. The processor is constructed by identical butterfly elements (BE's). The key concept to minimize the latency is that each BE generates its output data immediately after its input data become available, with 100% utilization of its arithmetic unit. We also introduce the real-valued FFT to perform the complex-valued FFT. We utilize a double linear array structure so that the parallel processing can be realized without communication between the linear arrays. As a result, the hardware amount of a single BE is reduced to half that of conventional designs. The latency of the proposed FFT processor is greatly reduced in comparison with conventional linear array FFT processors.

We present a navigation system using ultrasonic sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we develop a behavior-based system architecture, consisting of multi-layered behaviors. Basic behaviors required for the navigation of a mobile robot, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in a behavior-based system architecture. An extended potential filed method is developed to produce the appropriate velocity and steering commands for the behaviors of the robot. We demonstrate the capabilities of our system through real world experiments in unstructured dynamic office environments using an indoor mobile robot.

We have proposed a new omnidirectional image sensor COPIS (COnic Projection Image Sensor) for guiding navigation of a mobile robot. Its feature is passive sensing of the omnidirectional image of the environment in real-time (at the frame rate of a TV camera) using a conic mirror. COPIS is a suitable sensor for visual navigation in real world environment with moving objects. This paper describes a method for estimating the location and the motion of the robot by detecting the azimuth of each object in the omnidirectional image. In this method, the azimuth is matched with the given environmental map. The robot can always estimate its own location and motion precisely because COPIS observes a 360 degree view around the robot even if all edges are not extracted correctly from the omnidirectional image. We also present a method to avoid collision against unknown obstacles and estimate their locations by detecting their azimuth changes while the robot is moving in the environment. Using the COPIS system, we performed several experiments in the real world.

The demand for high-speed image processing is obvious in many real-world computations such as robot vision. Not only high throughput but also small latency becomes an important factor of the performance, because of the requirement of frequent visual feedback. In this paper, a high-performance VLSI image processor based on the multiple-valued residue arithmetic circuit is proposed for such applications. Parallelism is hierarchically used to realize the high-performance VLSI image processor. First, spatially parallel architecture that is different from pipeline architecture is considered to reduce the latency. Secondly, residue number arithmetic is introduced. In the residue number arithmetic, data communication between the mod mi arithmetic units is not necessary, so that multiple mod mi arithmetic units can be completely separated to different chips. Therefore, a number of mod mi multiply adders can be implemented on a single VLSI chip based on the modulus-slice concept. Finally, each mod mi arithmetic unit can be effectively implemented in parallel structure using the concept of a pseudoprimitive root and the multiple-valued current-mode circuit technology. Thus, it is made clear that the throughout use of parallelism makes the latency 1/3 in comparison with the ordinary binary implementation.

This paper proposes parallel VLSI processors for robotics based on multiple processing elements organized around multiple bus interconnection networks. The advantages of multiple bus interconnection networks are generality, simplicity of implementation and capability of parallel communications between processing elements, therefore it is considered to be suitable for parallel VLSI systems. We also propose the optimal scheduling formulated in an integer programming problem to minimize the delay time of the parallel VLSI processors.

This paper attempts to account for intelligibility of practices-based learning (so-called 'learning control') for skill refinement from the viewpoint of Newtonian mechanics. It is shown from an axiomatic approach that an extended notion of passivity for the residual error dynamics of robots plays a crucial role in their ability of learning. More precisely, it is shown that the exponentially weighted passivity with respect to residual velocity vector and torque vector leads the robot system to the convergence of trajectory tracking errors to zero with repeating practices. For a class of tasks when the endpoint is constrained geometrically on a surface, the problem of convergence of residual tracking errors and residual contact-force errors is also discussed on the basis of passivity analysis.