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自主式移动机器人系统的体系结构 总被引:6,自引:3,他引:6
本文在分析已有的几中多智能体协调模型的基础上,提出了一种用于自主式移动机器人系统的多智能体协调模型(离散)事件状态模型,用于组织协调自主式移动机器人系统中的传感器、规划、控制等智能体协调工作,确保自主式移动机器人在复杂、不断变化的环境中自主行驶,并在自主式移动机器人项目中较好地发挥了作用。 相似文献
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基于超声传感器阵列的陆地自主车测距 总被引:1,自引:0,他引:1
目前,超声波传感器主要用于低速运动的自主式移动机器人短距离测量,采用的测距方法多把串音干扰看作有用信号的干扰项,尽可能排除.基于远距离超声波传感器,本文利用串音干扰设计了一种适用于高速行驶的陆地自主车进行远距离测量的方法.实验结果表明此方法的可靠性和精度都能满足陆地自主车的距离探测要求. 相似文献
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To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc. 相似文献
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The collision-free trajectory planning method subject to control constraints for mobile manipulators is presented. The robot task is to move from the current configuration to a given final position in the workspace. The motions are planned in order to maximise an instantaneous manipulability measure to avoid manipulator singularities. Inequality constraints on state variables i.e. collision avoidance conditions and mechanical constraints are taken into consideration. The collision avoidance is accomplished by local perturbation of the mobile manipulator motion in the obstacles neighbourhood. The fulfilment of mechanical constraints is ensured by using a penalty function approach. The proposed method guarantees satisfying control limitations resulting from capabilities of robot actuators by applying the trajectory scaling approach. Nonholonomic constraints in a Pfaffian form are explicitly incorporated into the control algorithm. A computer example involving a mobile manipulator consisting of nonholonomic platform (2,0) class and 3DOF RPR type holonomic manipulator operating in a three-dimensional task space is also presented. 相似文献
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Anne-Dominique Jutard-Malinge Guy Bessonnet 《Journal of Intelligent and Robotic Systems》2000,29(3):233-255
The following study deals with motion optimization of robot arms having to transfer mobile objects grasped when moving. This approach is aimed at performing repetitive transfer tasks at a rapid rate without interrupting the dynamics of both the manipulator and the moving object. The junction location of the robot gripper with the object, together with grasp conditions, are partly defined by a set of local constraints. Thus, optimizing the robot motion in the approach phase of the transfer task leads to the statement of an optimal junction problem between the robot and the moving object. This optimal control problem is characterized by constrained final state and unknown traveling time. In such a case, Pontryagin"s maximum principle is a powerful mathematical tool for solving this optimization problem. Three simulated results of removing a mobile object on a conveyor belt are presented; the object is grasped in motion by a planar three-link manipulator. 相似文献
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Maximizing Reward in a Non-Stationary Mobile Robot Environment 总被引:1,自引:0,他引:1
The ability of a robot to improve its performance on a task can be critical, especially in poorly known and non-stationary environments where the best action or strategy is dependent upon the current state of the environment. In such systems, a good estimate of the current state of the environment is key to establishing high performance, however quantified. In this paper, we present an approach to state estimation in poorly known and non-stationary mobile robot environments, focusing on its application to a mine collection scenario, where performance is quantified using reward maximization. The approach is based on the use of augmented Markov models (AMMs), a sub-class of semi-Markov processes. We have developed an algorithm for incrementally constructing arbitrary-order AMMs on-line. It is used to capture the interaction dynamics between a robot and its environment in terms of behavior sequences executed during the performance of a task. For the purposes of reward maximization in a non-stationary environment, multiple AMMs monitor events at different timescales and provide statistics used to select the AMM likely to have a good estimate of the environmental state. AMMs with redundant or outdated information are discarded, while attempting to maintain sufficient data to reduce conformation to noise. This approach has been successfully implemented on a mobile robot performing a mine collection task. In the context of this task, we first present experimental results validating our reward maximization performance criterion. We then incorporate our algorithm for state estimation using multiple AMMs, allowing the robot to select appropriate actions based on the estimated state of the environment. The approach is tested first with a physical robot, in a non-stationary environment with an abrupt change, then with a simulation, in a gradually shifting environment. 相似文献
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In this paper we develop a technique to achieve robust high performance real-time wallfollowing behavior of a mobile robot in an indoor office environment, more specifically, in a corridor environment. The mobile robot achieves increasingly better performance by learning the environment's (most important) features in successive runs through it. This allows the robot to perform the task repeatedly, reliably, increasing the speed at which it is done after every step, without losing accuracy. We are basing our approach in the Spatial Semantic Hiearchy [Kuipers et. al. 1993]. 相似文献
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Fredy TungadiAuthor Vitae Lindsay Kleeman Author Vitae 《Robotics and Autonomous Systems》2011,59(6):428-443
This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes. 相似文献
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《Neural Networks, IEEE Transactions on》2006,17(5):1278-1287
In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies. 相似文献
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Proposed is a new approach to task segmentation in a mobile robot by a modular network SOM (mnSOM). In a mobile robot the
standard mnSOM is not applicable as it is, because it is based on the assumption that class labels are known a priori. In
a mobile robot, only a sequence of data without segmentation is available. Hence, we propose to decompose it into many subsequences,
supposing that a class label does not change within a subsequence. Accordingly, training of mnSOM is done for each subsequence
in contrast to that for each class in the standard mnSOM. The resulting mnSOM demonstrates good segmentation performance of
94.05% for a novel dataset. 相似文献