步行机器人中英文对照外文翻译文献(文档含英文原文和中文翻译)图1 远程脑系统的硬件配置图2 两组机器人的身体结构图3 传感器的两个水银定位开关图4 层次分类图5 步行步态该输入处理器是作为参考程序块和一个图像搜索窗口形象该大小的搜索窗口取决于参考块的大小通常高达16 * 16且匹配。

该处理器计算价值块在搜索窗口，还找到最佳匹配块，这就是其中的最低当目标平移时块匹配是非常有力的。

然而，普通的块匹配方法当它旋转时无法跟踪目标。

为了克服这一困难，我们开发了一种新方法，跟随真正旋转目标的图6 双足步行图6 双足步行图7 双足步行实验图8 一系列滚动和站立运动通过集成传感器网络转型的综合为了使上述描述的基本动作成为一体，我们通过一种方法来描述一种被认为是根据传感器状况的网络转型。

图9显示了综合了基本动作机器人的状态转移图：两足行走，滚动，坐着和站立。

这种一体化提供了机器人保持行走甚至跌倒时的problems and advance the study of vision-based behaviors, we have adopted a new approach through building remote-brained robots. The body and the brain are connected by wireless links by using wireless cameras and remote-controlled actuators.As a robot body does not need computers on-board,it becomes easier to build a lightweight body with many DOFS in actuation.In this research, we developed a two-armed bipedal robot using the remote-brained robot environment and made it to perform balancing based on vision and getting up through cooperating arms and legs. The system and experimental results are described below.2 The Remote-Brained SystemThe remote-brained robot does not bring its own brain within the body. It leaves the brain in the mother environment and communicates with it by radio links. This allows us to build a robot with a free body and a heavy brain. The connection link between the body and the brain defines the interface between software and hardware. Bodies are designed to suit each research project and task. This enables us advance in performing research with a variety of real robot systems[10].A major advantage of remote-brained robots is that the robot can have a large and heavy brain based on super parallel computers. Although hardware technology for vision has advanced and produced powerful compact vision systems, the size of the hardware is still large. Wireless connection between the camera and the vision processor has been a research tool. The remote-brained approach allows us to progress in the study of a variety of experimental issues in vision-based robotics.Another advantage of remote-brained approach is that the robot bodies can be lightweight. This opens up the possibility of working with legged mobile robots. AsFigure 4 shows some of the classes in the programming environent for remote-brained robot written in Euslisp. The hierachy in the classes provides us with rich facilities for extending development of various robots.4 Vision-Based BalancingThe robot can stand up on two legs. As it can change the gravity center of its body by controling the ankle angles, it can perform static bipedal walks. During static walking the robot has to control its body balance if the ground is not flat and stable.In order to perform vision-based balancing it is re-quired to have high speed vision system to keep ob-serving moving schene. We have developed a tracking vision board using a correlation chip[l3]. The vision board consists of a transputer augmented with a special LSI chip(MEP[14] : Motion Estimation Processor) which performs local image block matching.The inputs to the processor MEP are an image as a reference block and an image for a search window.The size of the reference blsearch window depends on the size of the reference block is usually up to 32 by 32 pixels so that it can include 16 * 16 possible matches. The processor calculates 256 values of SAD (sum of absolute difference) between the reference block and 256 blocks in the search window and also finds the best matching block, that is, the one which has the minimum SAD value.Clock is up to 16 by 16 pixels.The size of the search window depends on the size of the reference block is usually up to 32 by 32 pixels so that it can include 16 * 16 possible matches. The processor calculates 256 values of SAD (sum of absolute difference) between the reference block and 256 blocks in the search window and also finds the best matching block, that is, the one which has the minimum SAD value.Block matching is very powerful when the target moves only in translation. However, the ordinary block matching method cannot track the target when it rotates. In order to overcome this difficulty, we developed a new method which follows up the candidate templates to real rotation of the target. The rotated template method first generates all the rotated target images in advance, and several adequate candidates of the reference template are selected and matched is tracking the scene in the front view. It remembers the vertical orientation of an object as the reference for visual tracking and generates several rotated images of the reference image. If the vision tracks the reference object using the rotated images, it can measures the body rotation. In order to keep the body balance, the robot feedback controls its body rotation to control the center of the body gravity. The rotational visual tracker[l5] can track the image at video rate.5 Biped WalkingIf a bipedal robot can control the center of gravity freely, it can perform biped walk. As the robot shown in Figure 2 has the degrees to left and right directions at the ankle position, it can perform bipedal walking in static way.The motion sequence of one cycle in biped walking consists of eight phases as shown in Figure 6. One step consists of four phases; move-gravity-center-on-foot,lift-leg, move-forward-leg, place-leg. As the body is described in solid model, the robot can generate a body configuration for move-gravity-center-on-foot according to the parameter of the hight of the gravity center. After this movement, the robot can lift the other leg and move it forward. In lifting leg, the robot has to control the configuration in order to keep the center of gravity above the supporting foot. As the stability in balance depends on the hight of the gravity center, the robot selects suitable angles of the knees.Figure 7 shows a sequence of experiments of the robot in biped walking6 Rolling Over and Standing UpFigure 8 shows the sequence of rolling over, sitting and standing up. This motion requires coordination between arms and legs.As the robot foot consists of a battery, the robot can make use of the weight of the battery for the roll-over motion. When the robot throws up the left leg and moves the left arm back and the right arm forward, it can get rotary moment around the body. If the body starts turning, the right leg moves back and the left foot returns its position to lie on the face. This rollover motion changes the body orientation from face up to face down. It canbe verified by the orientation sensor.After getting face down orientation, the robot moves the arms down to sit on two feet. This motion causes slip movement between hands and the ground. If the length of the arm is not enough to carry the center of gravity of the body onto feet, this sitting motion requires dynamic pushing motion by arms. The standing motion is controlled in order to keep the balance.7 Integration through Building Sensor-Based Transition NetIn order to integrate the basic actions described above, we adopted a method to describe a sensor-based transition network in which transition is considered according to sensor status. Figure 9 shows a state transition diagram of the robot which integrates basic actions: biped walking, rolling over, sitting, and standing up. This integration provides the robot with capability of keeping walking even when it falls down.The ordinary biped walk is composed by taking two states, Left-leg Fore and Right-leg Fore, successively.The poses in ‘Lie on the Back’ and ‘Lie on the Face’are as same as one in ‘Stand’. That is, the shape ofthe robot body is same but the orientation is different.The robot can detect whether the robot lies on the back or the face using the orientation sensor. When the robot detects falls down, it changes the state to ‘Lie on the Back’ or ‘Lie on the Front’ by moving to the neutral pose. If the robot gets up from ‘Lie on the Back’, the motion sequence is planned to exe cute Roll-over, Sit and Stand-up motions. If the state is ‘Lie on the Face’, it does not execute Roll-over but moves arms up to perform the sitting motion.8 Concluding RemarksThis paper has presented a two-armed bipedal robot which can perform statically biped walk, rolling over and standing up motions. The key to build such behaviors is the remote-brained approach. As the experiments have shown, wireless technologies permit robot bodies free movement. It also seems to change the way we conceptualize robotics. In our laboratory it has enabled the development of a new research environment, better suited to robotics and real-world AI.The robot presented here is a legged robot. As legged locomotion requires dynamic visual feedback control, its vision-based behaviors can prove the effectiveness of the vision system and the remote-brained system. Our vision system is based on high speed block matching function implemented with motion estimation LSI. The vision system provides the mechanical bodies with dynamic and adaptive capabilities in interaction with human. The mechanical dog has shown adaptive behaviors based on distance。