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1、指令集成系统和协作机器人系统控制克拉克克瑞斯妥斐尔,弗雷易瑞克沃,琼斯亨瑞利和罗克史蒂芬姆斯坦福大学中的飞行驾驶员和宇航员的航天和航海航空与航天空间的机器人研究所部门网址: chrisc,ewf,hlj,rocksun-valley.stanford.edu摘要 在使用移动式机器人时可以提高其自治的三个问题在这篇文章中用实验性调查显示出来。即(1)单一的使用者与复合式机器人之间的集合工序和目标追踪的机器人轨道线路。在这次研究中,微型自主飞行器(MARS)发展成能为实验室性机器人的技术提供一种方案。实验的结果是:单一的使用者是可以命令包括任意碰撞运动和目标追踪在内的机器人的。关键词 移动式机器人
2、 飞行器 MARS测试平台 目标追踪 监测轨迹1.简介目前,远程机器人系统需要人们去操作单一的机器人。这些都是为了今后可以一人来控制多个机器人。例如,为使将来有益于少数人控制多数机器人的空间结构的建立。现在对一个远程机器人的控制技术已经达到了一个非常的高水平,但我们任然需要在对多组机器人的远程技术上狠下工夫。特别地,我们必须在提高机器人自制性方向上努力。那么这就需要提高以下几方面的功能:(1)为能够让人们从足够的信息作业决策和命令机器人去执行提供界面;(2)为能够让所有机器人的自由碰撞路线提供自主的、实时的构造;(3)为能够提供能追踪运动物体的机器人轨道路线。在先前的来工作中,我们提出了以下的
3、方案:琼斯提出一人控制多机的界面;克拉克设计了一个复合式机器人运动;弗雷能对物体运动估计最优方案设计了轨道线路。图1:MARS测试平台的飞行器在这幅图当中,展示出了对这些技术的综合性的阐明。第二部分对三个研究工程作出了扼要的描述。第三部分描述的是关于MARS平台及相关设备。第四部分详细地总结了作为一个集体系统目前的研究方向。第五部分是推论。2.技术性问题21人类界面为了操作一个复合性机器人的系统,这个人就必须要获得所有关于远程环境所涉及的信息,以便执行正确的指令,并且相应地获得对一个或几个机器人的执行指令性方案。琼斯提出了逐渐形成一种基于操作复合型机器人的人类和机器人对话的基面论述。特别是琼斯
4、提出了以下相应的论点:建立结构及其对话领域;创建输送有效对话的一种机器人能力基础设施;创建为处理对话的社会惯例决策的相应程序;开发一种允许操作者去执行关于机器人系统的对话界面。他的结论是一种在人群中普遍存在的工作定位对话的交流对话模型的实现。这种假设就类似对话在人类和机器人组合中扮演了一个重要的角色。为了提供给机器人环境的三维空间视觉,界面实现了使用GL。比如如图2所示的屏幕镜头。比起用声音还不如使用通过电子手段来进行界面的对话。当运用鼠标来在屏幕上选中它们时候,对话开始。运用界面来分析物体的特征,从而使机器人来判断物体。界面一直等到机器人完成对物体进行分析的有任务的各个环节,再返回后作出反映
5、。任务完成的结果以对话形式搁置于紧挨物体的位置处。使用的人可以从任务清单中进行选择,并且完成被派去的执行命令的的机器人、任务、或物体的命令。图2:人机界面的屏幕镜头在图2中的屏幕镜头是使用界面的一个例子,两个机器人(用自己圆柱体表示)和3个在工作面上的测试性物体。在屏幕左边,操作者命令机器人对物体进行处理。相应的主菜单上出现了执行的结果菜单。琼斯表示创造一个基础性对话能够控制大量的机器人,这是很有可能的。这种在三维世界中完成的相互性作用,为决定一个机器人在恰当的境地中的能力提供了一个可靠的方法,与此同时,它也使操作者能够去参加资源的处理和对机器人的相应设计工作。2.2运动平面在特定的区域当中,
6、当成组的机器人和变化移动的障碍物一起工作时,为了在竖直面上避免碰撞,但是在每个机器人之间进行不间断交流是不可能的,没有任何传感器系统可以提供全方位的信息。并且在移动性障碍物的活跃性环境,所以这个系统必须反应迅速。对于这种复合式机器人系统类型当中,对一个运动制作不需要综合的知识或者高效的带宽通信,以上这些仍旧要求实时部署。由克拉克开发出的一个运动系统达到了上面所述这种需求,哈苏和肯多开发出了基于设计者的规则系统。他们的工作阐明了一种方案:即一个动态显象管随机化运动设计者对周围存在动态障碍物和静态障碍物的单一机器人的操作。对于处置更多的机器人而言,那对动态显象管随机化运动设计就被延伸了。在这种情况
7、下,当机器人检测到彼此使用的传感器,以便进行彼此的交流沟通。机器人使用一个优化的系统,来对他们运动设计的协调化来避免相互的碰撞。每个机器人必须要创建一个关于对少数障碍物的知识性设计。在机器人的区域中对那些物体的设计,其中的运动设计问题是十分地简单的导致降低了设计的时间。当有新的事物进入机器的视线领域的时候,我们就必须要求进行一个重复性的设计以此来确保机器人轨迹不会发生自由的碰撞。一个涉及10个飞行器的模拟例子,如图3所示的5种静态障碍物。如图上所示的较小的圆代表微型飞行器。而十字叉代表目的地,较大的圆代表障碍物,用通向目的地的线条来表示机器人的轨迹。在一个特定的工作空间中对大量的机器人而言,运
8、动设计是十分有效的。而且它获得巨大的成功,甚至在涉及5到15个机器人的杂乱的环境中。其中还包括动态、静态障碍物,对环境变化作出迅速反应和实时重复设计的允许机器人有0.1S间隔的设计时间。虽然运动设计采用的是2维平面工作空间,但必须延伸到3维工作空间中。图3:运动设计对10个机器人和5个障碍物的模拟举例图(a)论述了飞行器,他们的目标和模拟前的障碍物。在(b)中,模拟实验刚开始的时候,所有的飞行器都进行了相应的设计,图(c)和(d)是实时计划在飞行中的典型例子(可以看出这充分地显示了通过障碍物的最初的轨迹线,但是因为机器人必须要跟得很近以便于去检测它们,而这些障碍物它们重新进行设计来逃逸飞行器。
9、最后在(e)当中,飞行器构造出了它们的最后轨迹,并且前方已经是它们的目的地了。图(f)显示几乎所有的飞行器都已经都达它们的目的地。2.3物体状态的轨迹设计物体的运动评估是自治性机器人的核心化能力。通过收集用摄相机监测获得的单个特殊的追踪数据来解决这一类问题。这一系统利用了安置在人来引导机器人的传感器(应用在航海技术的运动传感器和提供情景式状态的摄相机)上,所以不需要添加附加的负载。此外,摄相机的合并扫描激光装置或贮存环境模式的系统,即用单个摄相机来解决使用大量摄相机给系统带来的附加很长和错误容许间隙是非常有必要的。物体的运动估计使用应变式传感器,例如研究深入的单眼式。问题的关键特征是物体的位移
10、和速度不可能随时都可观测到,而统计量在摄相机的轨迹中起到重要作用。我们为了充分利用这种附属性的状况,弗雷逐渐形成了一种新的轨迹设计方案,这种方案可以最大限度地将背景信息提供给估量过滤器。根据这种新方案提出的三种主要问题是:视线的局限性,最佳轨迹的速度发展,以及对于不确定物体状况的最优解决方法的独立性参数的估计。新的轨道设计方法使用了金字塔式。在特定时间内获得最低限度的时间的限制,这样的目的是在最少时间获得不确定性估计,或产生了对不确定性的波动。由于一系列的连续性运行,恒定速度的操控,机器人的轨迹被参数化。为了平衡几种设计参数的差异,包括操作者的数目,发展空间的尺寸辨别和重复的数据,这种新的方法
11、设计出的轨道在大幅度降低设计成本和时间上获得了近乎完美的程度。另外,这种设计方案不断更新轨迹原来的数据从而作为新的数据,这一点是很容易在对物体的估计方面上达成一致性的。以上是一个模拟性例子,它使用新的设计方法去估测一个以0.00.015m/s的匀速运动物体,这种方法被称之为使用定时最低限度的不确定型费用。机器人有56s的操作时间,运行空间有5个间隔,金字塔式重复四次。如图4(a)中显示的结果为观察者的路径,它是真实目标路径和程序运行的目标估计。图4(b)显示了时间影响位移出现的错误,正如预料的那样,当错误出现在X轴坐标时,在Y坐标轴中这种错误持续性很小,这对物体排列的一致性需要花费更多的时间。
12、图(a)演示了最终的机器人轨道,图(b)显示由于时间的影响产生目标位移的错误。3.微型自主性测试平台微型自主性测试平台提供了一个二维空间来研究自主性飞行器。这个平台由一个12*9的花岗石桌面构成,有6个自主型机器人在表面运行。每个机器人都拥有置于空间外的自己的设计者。控制信号进程也是在外空间进行处理,由一个机器人通过无限RC信号将控制信号传递出去。一个可视性体系为三个摄相机提供给位置传感器去测试安装在机器人顶部表面的显示器装置。所有的在从MARS平台的交流信息都是通过实时实新的网络软件服务器进行的数据传输完成的。它的作用在公开发表或订阅的结构中。所以每个网点都能够接收到不同类型的数据。数据传输
13、如图5所示。图5:在微型自主性测试平台上的数字显示图5:左边表示的是硬件,右边表示的是图片使用者界面。在图片使用者界面中,用白色的圆柱体来表示机器人,在屏幕右上角位置的地方用黄色的线条来代表轨迹线路,通过运动设计者构成的机器人轨迹线路用红色的曲线来表示,用两条紫色的线条来表示跟踪机器人的监视范围。为了扩大视眼范围,将无线摄相机安装在微型自治式飞行器顶部测试平台来监测地面。这些单元具有商业性,并由2.4GHZ的无线立体发射机组合起来。用发光二极管(LED)通过跟踪轨迹来组装物体。4.集成系统为了将三种设计综合于一个体系当中,用如下更改系统来解决几种对应的技术性问题:用图片使用者界面来添加新的对话
14、能力,使用者享用新的职能。图片使用者界面为提供给使用者增加的功能相应必要信息添加新的显示符号体系。为了采取动态方案,安装在摄象头上的追踪性机器人拥有很高的优越性。从而使所有的机器人都能够构建围绕它的轨迹,从而建立所有要素间最广泛的协议通过机器人的时间同步性建立中央计时器下面的实验显示了三个研究效果的整体性,图片使用者界面进行的操控来逃避碰撞锁定定位目标的复合型机器人。图5 操控复合型机器人实验初期,在图片底部的机器人首先要被命令去跟随在左边有摄象机的机器人。(如图5a)。它必须与摄象机机器人保持30厘米的距离。紧接着,摄象机机器人用于监测目标,在这种情况下机器人在顶部(如图5b)必须要求它减少
15、位移估计的不确定值。执行任务的最后一个机器人要横穿这个工作面。(如图5c)。红色曲线代表设计者的轨道。让它与摄象机型机器人形成对立面。在图5d中,这个最后的机器人能感应到图5c的状况,这就使得它必须重新调整自己的路径。而产生的这一实时路径,在机器人的运动中并没有明显间断。最终地,图片使用者界面运用于命令三个机器人去完成有用的工作过程当中。它们在工作环境中正确地锁定目标,这个工作被第一个机器人成功地监控,与此同时,而第三个机器人能够达到它的位置目标,而它同时又操纵着在环境中的周围的另外物体。5.结论对于复合型机器人系统的可行性而言要三项基本工作是十分重要的。特别地,这个类型的工作可以由一个使用者
16、和多个机器人的界面去完成,其中还包括目标追踪的轨迹线路。以上这些也证明了可以将它们合并在一种实验性质的说明中,这种实验性质的说明说明它们作为更大更复杂的体系中的成分。工作合并实验性例证突显这三种基本工作的有效性。为此,我们可以得到以下结论:即这三项基本工作为协作性复合机器人系统显示了单一的使用者可以控制一组机器人去执行独立的工作,而这其中又包括了自由碰撞运动和目标追踪。An Integrated System for Command andControl of Cooperative Robotic SystemsChristopher M. Clark, Eric W. Frew, Henr
17、y L. Jones, & Stephen M. Rock Aerospace Robotics Lab Department of Aeronautics & Astronautics Stanford University chrisc, ewf, hlj, rocksun-valley.stanford.eduAbstract: Presented is an experimental investigation into three issues that enable increased autonomous functionality when using mobile robot
18、s. These issues are (1) interfacing a single user with multiple robots, (2) motion planning for multiple robots, and (3) robot trajectory generation for target tracking. For this research, the Micro Autonomous RoverS (MARS) test platform was developed that provides a means for implementing this tech
19、nology on laboratory robots to carry out tasks including collision-free which a single user is able to command a group of robots. Experimental results are presented in motion and target tracking.Key word: multi-robots rover MARS test platform target tracking trajectory monitor1. IntroductionCurrentl
20、y, remote robotic systems require many humans to operate a single robot. The goal for future systems is to require only one operator for many robots. For example, future space structure construction would benefit from the availability of a large group of robots that can be operated by a Figure 1: Ro
21、vers from the MARS test platform. Small groups of humans. While there has been a significant amount of research towards the operation of single remote robots, more work is still required towards the operation of groups of robots. In particular, an increased degree of autonomy must be given to the ro
22、bots. To realize this autonomy, a variety of fundamental capabilities must be enabled that include: (1) Providing an interface from which adequate information for decision-making is available to the human, and commands to one or several of the robots can be executed.(2) Providing autonomous, real-ti
23、me construction of collision-free trajectories for all robots in the group.(3) Providing robot trajectory generation that enables tracking of moving objects.In previous work, we addressed each of these issues: Jones 6 developed an interface that allows a single human to operate many robots; Clark 2
24、designed a multi-robot motion planner; and Frew 4 designed a trajectory generator that provides near-optimal solutions for object motion estimation.Figure 1 :Rovers from the MARS test plantformIn this paper, we present an integrated system demonstration of these technologies.The paper is organized a
25、s follows. Section 2provides a brief description of each of the three research projects. In Section 3, the Micro Autonomous RoverS (MARS) test platform and its application to this research is described. Section 4 details a final demonstration that summarizes the previous research as an integrated sy
26、stem. Conclusions are presented in Section 5.2. Technological Issues2.1. Human InterfaceFor a single human to operate a multi-robot system, the human must have access to all relevant information about the remote environment so that appropriate commands can be executed. Also, the human must be provid
27、ed with a means of executing these commands to one or several of the robots. In 6, Jones developed an interface based on dialogues between the human and the robots as an effective method for operating multiple robots. In particular, Jones addressed the following issues:- Establishing the structure a
28、nd scope of the dialogue- Creating a robot infrastructure capable of conducting an effective dialogue- Determining methods for dealing with the social conventions of dialogues- Developing an interface that allows the operator to carry out the dialogue with the robotic system.His result is an impleme
29、ntation of a dialogue interaction patterned after the task-oriented Dialogues common in human teams. The hypothesis is that similar dialogues can play a useful role within human-robot teams.The interface was implemented using OpenGL to provide a three-dimensional view of the robot environment. An ex
30、ample screen-shot is shown in Figure 2. Dialogue through the interface takes place electronically rather than through voice. The dialogue begins when objects are selected by clicking on them on the screen. The interface then resolves the identity of the object and the robot that sensed the object. T
31、he interface waits until a response from the correct robot has been returned in the form of a list of tasks that the robot can accomplish on that object. This list is then displayed in a dialog that pops up next to the object. The user can select from this list of tasks, and the complete command of
32、robot/task/object is sent to the robot for execution.The screen-shot in Figure 2 provides an example of the interface in use. Two robots (denoted by white cylinders) and 3 objects are located on the test bed workspace. The operator has queried the robot agents to determine what tasks can be performe
33、d on the object at the left side of the screen. A pop-up menu has appeared with a list of the query results.Figure 2: Screen shot of the human-robot interface.Jones showed that it is possible to build a dialogue-based interaction that enables the control of multiple robots. This interaction, as impl
34、emented in a virtual three-dimensional world, provided an intuitive point-and-click method for determining the capabilities of the robot in the appropriate context, and enabled the operator to participate in the resource management and task planning for the robots.2.2 Motion PlanningWhen large group
35、s of robots and moving obstacles are working together within a designated area, high-level motion planning is required to avoid collisions. Continuous communication between all robots may not be feasible, and no system of sensors can provide global knowledge. Also, to function in a dynamic environme
36、nt with moving obstacles, the system must be able to react quickly. For this type of multi-robot system, a motion planner that does not need global knowledge or high bandwidth communication, but that can still plan in real-time, is required.A motion planning system that meets this requirement was de
37、veloped by Clark 2. The algorithm presented was based on the planner developed by Hsu and Kindel 5. Their work demonstrates the use of a Kino dynamic Randomized Motion Planner for a single robot maneuvering around stationary and moving obstacles.To handle more than one robot, the Kino dynamic Random
38、ized Motion Planner was extended. In the extended planner, when robots detect one another using local sensors, they communicate with eachOther,using a priority system, the robots coordinate their motion plans to avoid collisions. Each robot creates a plan with knowledge of only the few obstacles sur
39、rounding it. By planning around only those objects within the robots local area, the motion planning problem is greatly simplified leading to decreased planning times. When new objects enter the robots field of view, a re-plan is called for to ensure that the robots trajectory is collision-free.An e
40、xample of a simulation involving 10 rovers, and 5 stationary obstacles is provided if Figure 3. Smaller circles represent the micro-rovers as viewed from above, while crosses represent goal locations and larger circles represent obstacles. Trajectories constructed by each robots motion planner are i
41、ndicated with lines that lead to goal locations.The motion planner demonstrated its effectiveness in planning for a large number of robots within a bounded workspace. It planned with a high probability of success, even in cluttered environments involving 5 to 15 robots, Stationary obstacles and movi
42、ng obstacles. Planning times on the order of 0.1 s allowed the robots to re-plan in real-time and react quickly to changes in the environment. Although the motion planner was applied to a 2D workspace, it should be noted that the planner is extendible to 3D workspaces.Figure 3: Motion planning simul
43、ation exampleInvolving in10 robots and 5 obstacles. Figure a) illustrates rovers, their goals, and obstacles before the simulation. In b), the simulation has just begun and all rovers have constructed their first plan. Examples of real-time planning on the fly are shown in c) and d). (Note that some
44、 initial trajectories pass through obstacles, but as the robots come close enough to sense them, they replant to avoid them.) Finally, in e) the rovers have constructed their last trajectory and are headed towards their respective goal location. Figure f) shows all but one rover having reached their
45、 goal location.2.3 Trajectory Design for Object StateEstimationObject motion estimation is a core capability of autonomous robots. One solution to this problem can be achieved with a single camera by fusing image track data from a single feature with camera motion measurements. Such a system takes a
46、dvantage of sensors already expected on a human-guided robot (motion sensors that enable navigation and cameras that provide situational awareness) and therefore requires little additional payload. Furthermore, a single camera solution adds redundancy and fault tolerance to current systems that use
47、multiple cameras, combinations of cameras and scanning lasers, or stored environment models 3 7.Object motion estimation using bearing sensors such as monocular vision has been well studied 1 8. The key features of this problem are that the object position and velocity are unobservable at any instan
48、t in time and that performance of the estimator is a strong function of the camera trajectory. Exploiting this dependence, Frew 4 developed a new trajectory design method that maximizes the information content provided to the estimation filter. The three main issues addressed by this new method are inclusion of the monocular vision field of view constraints, the quick generation of near-optimal trajectories, and theDependence of the optimal solution on the uncertain object state the very parameter being estimated.The new trajectory design method uses a pyramid, breadth-first search to ge