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1.
This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks. The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with static or moving obstacles.  相似文献   

2.
针对移动机器人在复杂未知环境下运行时易出现局部死锁和路径冗余的问题,提出了一种结合预测和模糊控制的局部路径规划方法,并对规划过程中出现的问题提出了解决策略。将普通避障行为与沿墙走行为融合为一个模糊控制器,通过内部规则实现行为交替。针对模糊控制方法存在多U型障碍物的死锁问题,提出了累加转角和的判断方法,帮助机器人逃脱死锁状态。加入陷阱预测机制,使机器人能在一定程度上克服传感器测量范围的局限性,预测前方是否可行并做出决策,减少冗余路段。设计了基于人工势场法的有限状态机,解决了因障碍物对称而无法确定方向所产生的路径冗余。在MATLAB平台中进行了仿真测试,验证了所设计方法的可行性和有效性。  相似文献   

3.
This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning: advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A* path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.  相似文献   

4.
This article describes a sensor-based obstruction avoidance technique. This technique, if implemented on the on-board computer of a mobile robot, would enable the robot to move through an unknown environment. The proposed approach is driven by sensory data. The robot thus senses and adapts to the changes in the environment. The software also does path planning. As more information about the environment is obtained the robot's path planning capabilities improve. Illustrative examples are used to describe the algorithms.  相似文献   

5.
Most current mobile robots are designed to determine their actions according to their positions. Before making a decision, they need to localize themselves. Thus, their observation strategies are mainly for self-localization. However, observation strategies should not only be for self-localization but also for decision making. We propose an observation strategy that enables a mobile robot to make a decision. It enables a robot equipped with a limited viewing angle camera to make decisions without self-localization. A robot can make a decision based on a decision tree and on prediction trees of observations constructed from its experiences. The trees are constructed based on an information criterion for the action decision, not for self-localization or state estimation. The experimental results with a four legged robot are shown and discussed.  相似文献   

6.
We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.  相似文献   

7.
张尧  陈卫东 《机器人》2005,27(2):173-177,182
针对移动机器人路径规划与导航的实际应用,设计了一个基于全景视觉的移动机器人路径规划导航系统.首先,对导航系统的体系结构和功能进行描述.然后,分别就如何采用全景视觉传感器进行环境探索与地图创建,基于回归神经网络的广度优先搜索法和Voronoi骨架图法两种路径规划算法原理,以及如何实现按规划路径实施导航这三个方面进行了详细阐述.最后,结合实际机器人进行导航实验,评估导航系统的性能和路径规划算法的有效性.  相似文献   

8.
There has been significant interest in automating testing on the basis of an extended finite state machine (EFSM) model of the required behaviour of the implementation under test (IUT). Many test criteria require that certain parts of the EFSM are executed. For example, we may want to execute every transition of the EFSM. In order to find a test suite (set of input sequences) that achieves this we might first derive a set of paths through the EFSM that satisfy the criterion using, for example, algorithms from graph theory. We then attempt to produce input sequences that trigger these paths. Unfortunately, however, the EFSM might have infeasible paths and the problem of determining whether a path is feasible is generally undecidable. This paper describes an approach in which a fitness function is used to estimate how easy it is to find an input sequence to trigger a given path through an EFSM. Such a fitness function could be used in a search-based approach in which we search for a path with good fitness that achieves a test objective, such as executing a particular transition, and then search for an input sequence that triggers the path. If this second search fails then we search for another path with good fitness and repeat the process. We give a computationally inexpensive approach (fitness function) that estimates the feasibility of a path. In order to evaluate this fitness function we compared the fitness of a path with the ease with which an input sequence can be produced using search to trigger the path and we used random sampling in order to estimate this. The empirical evidence suggests that a reasonably good correlation (0.72 and 0.62) exists between the fitness of a path, produced using the proposed fitness function, and an estimate of the ease with which we can randomly generate an input sequence to trigger the path.  相似文献   

9.
提出了一种新的路径搜索算法——"触觉感知法"来实现机器人在未知静态与动态环境情况下的路径搜索。该方法不需要提供地图信息,机器人仅收集目标点的距离和方位信息以及通过自带传感器作为触觉器收集周围局部环境信息。机器人以BP神经网络作为决策器,经过训练,可以在静态和动态环境中搜索出一条光滑无碰撞且便捷并能有效避开动态障碍物的运动轨迹。对所提出的方法进行了仿真实验,仿真结果表明算法在静态和动态环境下均能有高效率的路径搜索表现。  相似文献   

10.
Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.   相似文献   

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