【文章內(nèi)容簡(jiǎn)介】 oys that are difficult to machine at room temperature can be machined more easily at elevated temperatures. In thermally assisted machining (hot machining), the source of heat—a torch, induction coil, highenergy beam (such as laser or electron beam), or plasma arc—is forces, (b) increased tool life, (c) use of inexpensive cuttingtool materials, (d) higher materialremoval rates, and (e) reduced tendency for vibration and chatter. It may be difficult to heat and maintain a uniform temperature distribution within the workpiece. Also, the original microstructure of the workpiece may be adversely affected by elevated temperatures. Most applications of hot machining are in the turning of highstrength metals and alloys, although experiments are in progress to machine ceramics such as silicon nitride. SUMMARY Machinability is usually defined in terms of surface finish, tool life, force and power requirements, and chip control. Machinability of materials depends not only on their intrinsic properties and microstructure, but also on proper selection and control of process variables. Abstract: A bipedal architecture is highly suitable for a robot built to work in human environments since such a robot will find avoiding obstacles a relatively easy task. However, the plex dynamics involved in the walking mechanism make the control of such a robot a challenging task. The zeromoment point (ZMP) trajectory in the robot’s foot is a signi?cant criterion for the robot’s stability during walking. If the ZMP could be measured online then it bees possible to create stable walking conditions for the robot and here also stably control the robot by using the measured ZMP, values. ZMP data is measured in realtime situations using a biped walking robot and this ZMP data is then modelled using an adaptive neurofuzzy system (ANFS). Natural walking motions on ?at level surfaces and up and down a 10176。 slope are measured. The modelling performance of the ANFS is optimized by changing the membership functions and the consequent part of the fuzzy rules. The excellent performance demonstrated by the ANFS means that it can not only be used to model robot movements but also to control actual robots. 1 Introduction The bipedal structure is one of the most versatile setups for a walking robot. A biped, robot has almost the same movement mechanisms as a human and it able to operate in environments containing stairs, obstacles etc. However, the dynamics involved are highly nonlinear, plex and unstable. Thus, it is dif?cult to generate a humanlike walking motion. The realisation of humanlike walking robots is an area of considerable activity [1–4]. In contrast to industrial robot manipulators, the interaction between a walking robot and the ground is plex. The concept of a zeromoment point (ZMP) [2] has been shown to be useful in the control of this interaction. The trajectory of the ZMP beneath the robot foot during a walk is after taken to be an indication of the stability of the walk [1–6]. Using the ZMP we can synthesise the walking patterns of biped robots and demonstrate a walking motion with actual robots. Thus, the ZMP criterion dictates the dynamic stability of a biped robot. The ZMP represents the point at which the ground reaction force is taken to occur. The 4 location of the ZMP can be calculated using a model of the robot. However, it is possible that there can be a large error between the actual ZMP value and the calculated value, due to deviations in the physical parameters between the mathematical model and the real machine. Thus, the actual ZMP should be measured especially if it is to be used in a to parameters a control method for stable walking. In this work actual ZMP data taken throughout the whole walking cycle are obtained from a practical biped waling robot. The robot will be tested both on a ?at ?oor and also on 10 slopes. An adaptive neurofuzzy system (ANFS) will be used to model the ZMP trajectory data thereby allowing its use to control a plex real biped walking robot. 2 Biped walking robot Design of the biped walking robot We have designed and implemented the biped walking robot shown in Fig. 1. The robot has 19 joints. The key dimensions of the robot are also shown in Fig. height and the total weight are about 380mm and 1700 g including batteries, respectively. The weight of the robot is minimised by using aluminium in its construction. Each joint is driven by a RC servomotor that consists of a DC motor, gears and a simple controller. Each of the RC servomotors is mounted in a linked structure. This structure ensures that the robot is stable (. will not fall down easily) and gives the robot a humanlike appearance. A block diagram of our robot system is shown in Fig. 2. Out robot is able to walk at a rate of one step (48mm) every s on a ?at ?oor or an shallow slopes. The speci?cations of the robot are listed in Table 1. The walkingmotions of the robot are shown in Figs. 3– Figures 3 and 4 are show front and side views of the robot, respectively when the robot is on a ?at surface. Figure 5 is a snapshot of the robot walking down a slope whereas Fig. 6 is a snapshot of the robot walking up a slope. The locations of the j