Experimental study on autonomous mobile robot acquiring optimal action to avoid moving obstacles

The principal aim of this study was to show how an autonomous mobile robot can acquire the optimal action to avoid moving multiobstacles through interaction with the real world. In this paper, we propose a new architecture using hierarchical fuzzy rules, a fuzzy evaluation system, and learning automata. By using our proposed method, the robot autonomously acquires finely tuned behavior which allows it to move to its goal and avoid moving obstacles by using the steering and velocity control inputs simultaneously. We also show experimental results which confirm the feasibility of our method.     

一种混联码垛机器人智能避障轨迹规划与仿真

由于码垛机器人应用环境复杂、不确定条件多等诸多问题,针对现有的避障轨迹规划算法存在的划分空间上的繁琐情况,提出了一种智能避障轨迹规划算法,包括障碍物信息提取和避障轨迹设计,最大程度降低障碍物信息与轨迹设计部分的耦合度,达到减少动态空间下频繁划分空间的目的.最后在OPENGL软件上研发的三维仿真平台上进行运动学仿真与验证.仿真结果表明,改进算法规划出的轨迹能有效避开障碍物且轨迹符合预期的要求,充分验证了轨迹规划算法的可行性和有效性.     

一种移动机器人的路径规划算法

本文提出一种移动机器人路径规划最短切线路径算法。依据此算法,机器人能顺利地避开障碍物到达目标位置,其原理简单,计算快捷,容易实现。仿真结果验证了它的有效性和实用性。     

PRIAMOS: An experimental platform for reflexive navigation

In this paper the new mobile robot system PRIAMOS is presented. The system emphasizes on fast navigation algorithms. In a (partially) unknown or changing environment fast perception and reaction is necessary for efficient task execution. Low level mapping and planning modules are used to span the period until higher control layers have enough time to react. The first part of this paper briefly describes the mechanical and technical construction of PRIAMOS. After an explanation of the software architecture the solutions for motion control and reflexive navigation, as realized in PRIAMOS, are given.     

移动机器人模糊逻辑控制系统避障研究

本文对全区域覆盖的局部路径规划,采用了一种模糊控制算法,利用模糊控制算法自身所具有的鲁棒性和基于生物学上的感知一动作的行为相结合。对于移动机器人的避障系统提出了充分接近障碍的避障策略,并对相关理论和实现方法作了深入的研究。     

非完整移动机械臂的避障运动规划

针对有空间障碍物避免的移动式操作机器人系统运动规划问题,提出了一种基于特殊的人工势函数,使用局部距离信息实现非完整移动机械臂系统实时避障运动规划方法,并且用Lyapunov定理证明了闲环系统的稳定性。用提出的方法对非完整移动机械臂系统进行仿真．仿真结果表明了它的正确有效性。     

Obstacle avoidance control of redundant robots using variants of particle swarm optimization

Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two algorithms are hybrid version of the first two. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside velocity update routine in updating particles' position. The computational results reveal that PSO-W yields better performance in terms of faster convergence and accuracy.     

移动机器人路径规划技术的现状与发展

移动机器人技术是近年来的研究热点,路径规划技术是移动机器人技术研究中的一个重要领域。路径规划分为基于模型的环境已知的全局路径规划和基于传感器的环境未知的局部路径规划。该文详细地叙述了移动机器人路径规划技术的分类和发展现状,全局路径规划和局部路径规划中的各种方法,具体地分析了各种方法的算法过程,并指出了各种方法的优缺点,以及各种方法的改进的办法,最后对移动机器人路径规划技术的未来的发展趋势进行了展望。     

移动机器人在障碍物具有不确定性时的运动规划

本文提出了一种移动机器人在实时避碰中对移动障碍物的运动不确定性的处理方法.该方法主要考虑了两个不确定性来源：移动障碍物运动速度上的不确定性和运动方向上的不确定性.用概率统计的方法来为不确定性建模.并与一种基于相对速度的在线避碰方法结合起来对移动障碍物避碰.通过这种方法可以使机器人对移动障碍物的避碰更有效率.     

未知环境下的移动机器人定位及实时避障

针对未知环境下移动机器人实时动态避障及定位问题，考虑里程计定位的无界累加误差和动态障碍物环境下实时障碍躲避需要，提出了一种可行的避障定位的策略。该策略融合了机器人内部传感器、里程计、电子罗盘和激光测距仪的同步和异步信息，合理地解决了常规定位过程中的方向迷失问题，对于静态和动态障碍物都能很好地实时躲避，具有很强的抗干扰性和较高的定位精度。实验证明了该方法的有效性和实用性.     

四轮移动机器人智能预瞄轨迹跟踪控制

针对后轮驱动四轮移动机器人存在非完整约束、模型复杂等特点,提出一种基于多预瞄点的轨迹跟踪混合控制算法。该算法设计从仿人驾车的角度出发,根据不同路况通过模糊控制在线调节机器人的速度、预瞄点个数和预瞄距离。采用免疫控制方法在线整定PID控制器的参数。从控制稳定性角度出发提出轨迹偏离度评价指标。仿真结果表明该控制器的有效性。     

一种基于相对坐标系下移动机器人动态实时避碰的新方法

本文提出了一种机器人在动态环境下的动态实时避碰的新方法．此方法是基于相对坐标系,在加速度空间中,通过动态实时地调整机器人自身速度的大小和方向使其离开碰撞区域,即碰撞危险区域,达到与动、静态障碍物之间的避碰．仿真实验验证了此方法的有效性．     

Stereo perception and dead reckoning for a prototype lunar rover

This paper describes practical, effective approaches to stereo perception and dead reckoning, and presents results from systems implemented for a prototype lunar rover operating in natural, outdoor environments.The stereo perception hardware includes a binocular head mounted on a motion-averaging mast. This head provides images to a normalized correlation matcher, that intelligently selects what part of the image to process (saving time), and subsamples the images (again saving time) without subsampling disparities (which would reduce accuracy). The implementation has operated successfully during long-duration field exercises, processing streams of thousands of images.The dead reckoning approach employs encoders, inclinometers, a compass, and a turn-rate sensor to maintain the position and orientation of the rover as it traverses. The approach integrates classical odometry with inertial guidance. The implementation succeeds in the face of significant sensor noise by virtue of sensor modelling, plus extensive filtering.The stereo and dead reckoning components are used by an obstacle avoidance planner that projects a finite number of arcs through the terrain map, and evaluates the traversability of each arc to choose a travel direction that is safe and effective. With these components integrated into a complete navigation system, a prototype rover has traversed over 1 km in lunar-like environments.     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

基于Matlab的机器人避障仿真软件设计

为了提高机器人避障算法开发效率,设计了一种基于Matlab的机器人避障算法设计与试验仿真软件。该软件分为用户图形界面、避障算法系统模型两大部分。其中用户图形界面是基于Matlab 图形用户界面( GUI)实现的,能够实现参数设置、结果展示等功能,给用户提供了一个操作及管理平台;避障算法系统模型是本软件实现的关键,包括探测传感器模拟和机器人运动模拟,其通过Matlab语言编程和Simulink模块组建实现。全面阐述了仿真软件的系统建模、软件编程和用户图形界面设计,并对仿真软件实现所采用的关键技术进行了详细说明。仿真实例表明,该软件运行平稳,能清晰直观地展现机器人的避障过程。     

基于自组织LMBPNN的移动机器人路径规划器

提出一种自组织LMBP神经网络,并将之用于移动机器人免碰路径规划。该算法首先用基于距离传感器的底层局部路径规划器生成初始路径,然后用自组织神经网络将该路径进行样本数据分类,之后将自组织神经网络的权值作为LMBP的输出样本,移动机器人的起始点与目标点作为LMBP神经网络的输入样本进行学习。这样,不但解决了三层LMBP样本若庞大则增加存贮、运行成本,以及数据冗余问题,并且随着机器人对未知环境探索的增多,所构建的地图越趋丰满。仿真结果说明该方法很好效。     

不满足非完整约束的轮式移动机器人鲁棒镇定

以unicycle类型轮式移动机器人为对象,在理想非完整约束条件被破坏的条件下,采用横截函数方法和Lyapunov重设计技术设计了鲁棒实际镇定律。构造有界横截函数,再结合轮式移动机器人运动学模型对标准SE（2）群运算的左不变性,对误差系统设计光滑指数镇定律,实现标称系统实际镇定;用Lyapunov重设计方法设计修正项,使得闭环系统对滑动干扰鲁棒。仿真结果验证了所设计控制律的有效性。     

基于反步法的移动机器人鲁棒跟踪控制

以四轮移动机器人为研究对象,建立了机器人完整的数学模型,包括运动学模型、动力学模型以及驱动电机模型。在机器人数学模型的基础上,采用反步法的思想设计具有全局收敛特性的鲁棒轨迹跟踪控制器,设计中考虑了驱动电机模型使控制器更符合实际控制要求,并将其分解为运动学控制器、动力学控制器以及电机控制器三部分,降低了控制器设计的难度。构造了系统的李雅普诺夫函数,证明了该类型移动机器人在所得控制器作用下,能实现对给定轨迹的全局渐近追踪。仿真实验结果表明基于反步法的控制器是有效的。