柔性内镜介入机器人模糊PID控制研究

柔性内镜以其损伤小、检查直观等优势在消化道疾病的诊断与治疗上得到广泛的应用,但其操作困难,因此研发有内镜机器人辅助医生进行内镜操作。在镜下介入手术操作过程中,人体生理活动增加了内镜远程操作的难度。针对生理活动对目标靶点位置的影响,分析内镜弯曲段结构,建立了柔性内镜机器人的运动学模型,并引入模糊PID控制策略来适应人体解剖结构、内镜参数等不确定因素影响,使内镜自动跟踪病灶靶点,避免人重复小范围的跟踪动作,提高控制精度。仿真表明,相对于传统PID控制算法,模糊PID控制算法可加快系统响应,降低系统稳态误差。     

基于BIM的建筑机器人自主导航策略优化研究

针对建筑机器人在复杂环境下自主导航过程中的安全性、通过性等问题,提出了一种应用BIM(building information model)信息与激光雷达获取的现实环境信息进行导航并保证路径最优的方法。根据BIM信息优化A*算法,考虑建筑机器人的通过性与安全性并删除了路径中的冗余节点,在节点间根据雷达信息优化动态窗口法,有效保证了路径最优,提升了安全性、缩短了运行时间并减少了转折角度。加入超宽频定位模块,能够很好地消除机器人移动产生的累计误差。实验结果表明,改进后A*算法的搜索时间比改进前减少了92%,路径转折角度减少50%,路径长度比原始动态窗口法减少13.5%,所需时间仅比无障碍物时增加3.7%。     

A Wireless Robotic Endoscope for Gastrointestine

In recent years, an intelligent noninvasive endoscope has been felt necessary for early diagnosis of malignant tumor in gastrointestine (GI). It is our purpose to develop a microwireless robotic endoscope to examine the human GI. The presented robot's diameter and length is 10 and 190 mm, respectively. A locomotion principle based on biomimetic earthworm is adopted for a higher adaptability to the GI. The robot is composed of three linear driving cells. A micromotor, a reducer, and a microscrew pair mechanism are integrated into each cell. Multijoints are used to connect every two driving cells for robot's high flexibility. The robot's energy is continuously supplied in real time by an energy transmitting system based on electromagnetic coupling. Experiments on the energy transmission and locomotion are performed to test the power transferring volume and locomotion effect in GI. The experiments indicate that the minimum received power, 400 mW, is obtained in a cylindrical space with 200 mm diameter and 200 mm length. In vitro experiments in a pig intestine indicate that this robot can move forward or backward effectively. This paper provides a good prototype for the deeper research on locomotion theory and energy transferring technology in vivo in future.     

Decision-Theoretic Planning for Autonomous Robotic Surveillance

In this paper, we introduce a decision-theoretic strategy for surveillance as a first step towards automating the planning of the movement of an autonomous surveillance robot. In our opinion, this particular application is interesting in its own right, but it also provides a test-case for formalisms aimed at dealing both with (low-level) sensor, localisation, and navigation uncertainty and with uncertainty at a more abstract planning level. After a brief discussion of our view on surveillance, we describe a very simple formal model of an environment in which the surveillance task has to be performed. We use this model to illustrate our decision-theoretic strategy and to compare this strategy with other proposed strategies. We treat several simple examples and obtain some general results.     

A Robotic Excavator for Autonomous Truck Loading

Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavator's software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.     

Geometrically Exact Models for Soft Robotic Manipulators

Unlike traditional rigid linked robots, soft robotic manipulators can bend into a wide variety of complex shapes due to control inputs and gravitational loading. This paper presents a new approach for modeling soft robotic manipulators that incorporates the effect of material nonlinearities and distributed weight and payload. The model is geometrically exact for the large curvature, shear, torsion, and extension that often occur in these manipulators. The model is based on the geometrically exact Cosserat rod theory and a fiber reinforced model of the air muscle actuators. The model is validated experimentally on the OctArm V manipulator, showing less than 5% average error for a wide range of actuation pressures and base orientations as compared to almost 50% average error for the constant-curvature model previously used by researchers. Workspace plots generated from the model show the significant effects of self-weight on OctArm V.      

Biologically-Inspired Collective Control for an Autonomous Robotic Arm

Biological collective control architectures and simple control principles used in nervous systems provide novel alternative approaches for the design of fault-tolerant, adaptable real-world robotic systems that have traditionally relied on centralized control. In this research, a robotic arm composed of multiple identical segments in a collective computational architecture was tested for its ability to produce adaptive pointing and reaching behavior. The movement rules for these robotic arm segments were derived from reflex arc principles in the human nervous system. These arm segments received no central directions and used no direct informational exchange, but rather the arm was sensor-driven at its leading segment in a way that maximized pointing accuracy of the arm. The remaining non-leading segments in the arm were moved in a sequential order using only sensed locally-available movement information about neighboring segments.Pointing and reaching behavior was observed in experiments with and without obstacles to movement. Because such behavior was not specified within each segment, the overall limb behavior emerged due to the interaction and coordination of all segments, rather than due to any single segment, centrally controlled influence, or explicit inter-segmental method of communication.     

Integrative Tracking Control Strategy for Robotic Excavation

Automated excavation is hard to achieve due to several inherent problems such as resistive force acting against the bucket, non-homogenous dynamics of various excavation media, and nonlinearities of the excavator’s hydraulics system. To deal with this issue, this paper provides an integrative control strategy for successful autonomous excavation that considers the mutually associated factors, i.e., position, contour, and force control. For the position tracking, a non-linear PI controller was designed to track the position of individual actuators of the excavator and thereby control the bucket tip’s position. In addition, the contour control technique was applied to achieve an optimal excavation path to minimize contour errors. Finally, to compensate for the ground resistive force during digging tasks, a force impedance controller was designed along with the time-delayed control that reduces the effect of dynamic uncertainties. Experimental results with a modified mini-wheeled excavator show that the developed integrative tracking control strategy can provide a comprehensive solution to improving the tracking performance for autonomous excavation that can simultaneously deal with the critical components of position, contour, and force control.

     

一种用于自律搬运车导航的混合式视觉方法

For the autonomous guided vehicle (AGV) used mainly in unfixed work fields, a machine vision method was proposed for the navigation system, in which a series of navigation-signs are placed along the travel route. The navigation system detects and recognizes these signs, and accordingly informs the travel control system. In order for the navigation to have balanced ability of 1) covering a large area and 2) recognizing details of the sign, the proposed vision method was designed to be a hybrid one, using both the stereo vision and the traditional 2D template matching. The former implemented a coarse recognition function for above 1), and the later implemented a fine recognition function for above 2). The results from the coarse recognition were used in the fine recognition for the gaze control to input suitable 2D image of the signs. Experiments on a prototype system show the feasibility of the proposed hybrid method in achieving the objective specifications for a typicalb AGV.     

基于多模特征深度学习的机器人抓取判别方法

针对智能机器人抓取判别问题,研究多模特征深度学习与融合方法.该方法将测试特征分布偏离训练特征视为一类噪化,引入带稀疏约束的降噪自动编码（Denoising auto-encoding, DAE）,实现网络权值学习;并以叠层融合策略,获取初始多模特征的深层抽象表达,两种手段相结合旨在提高深度网络的鲁棒性和抓取判别精确性.实验采用深度摄像机与6自由度工业机器人组建测试平台,对不同类别目标进行在线对比实验.结果表明,设计的多模特征深度学习依据人的抓取习惯,实现最优抓取判别,并且机器人成功实施抓取定位,研究方法对新目标具备良好的抓取判别能力.     

仿真机器鱼抢球大作战比赛策略的研究

针对北大机器鱼平台中“抢球大作战”项目控制量多,多机器鱼间协作及对抗性强,以及场地设置和比赛规则变化快等特点,设计一套灵活、有效,并且极具对抗性的多机器鱼控制策略,实现机器鱼之间的协作分工,最终赢得比赛的胜利.由于环境的复杂多变,为解决控制策略的优化,提出先将机器鱼进行角色分工,并依据场地中水球所在位置将场地进行区域划分,再依据时间将比赛进行阶段划分.最后综合考虑这三个因素,给出多机器鱼协作抢球的有效策略,并在“2012中国水中机器人大赛暨首届国际水中机器人公开赛”中取得一等奖的好成绩,充分验证了改进策略的稳定性和有效性.