基于近似Voronoi图的移动机器人实时路径规划

运用Voronoi图理论及人工势场理论,研究了一种基于近似Voronoi图的移动机器人实时路径规划的方法,用来实现未知室内环境中移动机器人的自主导航。该方法朝向预先定义的目标点位置来探测室内环境,生成近似Voronoi图,同时利用人工势场法进行避障,生成一条能达到目标点的安全、光滑路径。仿真结果表明,该方法简单且易于实现,同时能够减少规划时间。     

一种车型机器人路径规划方法

在自主移动机器人的许多应用中,路径规划技术顺序地设置一套分散的路径点来引导机器人以最短的时间从起始位置到达目标点。针对移动机器人路径规划问题,提出了一种非完整型机器人路径规划技术,该技术采用基本原子操纵方法来解决车型机器人路径规划问题,并采用平滑路径规划方法来产生更多的连续路径用以解决基本原子操纵技术在做路径规划时具有很不连续的缺点从而为机器人获得最优路径。仿真结果证明了该方法的有效性和实用性。     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

Global Navigation in Dynamic Environments Using Case-Based Reasoning

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.     

基于动态精简式混合地图的移动机器人自主探索

针对未知环境下移动机器人自主探索和地图创建问题,在机器人操作系统的框架下,提出一种基于动态精简式混合地图的移动机器人自主探索方法.首先,提出一种基于几何规则的候选目标点生成方法,用于快速提取当前的前沿目标点;然后,从信息收益和路径成本的角度,引入一种改进的效用函数来评价候选目标点;最后,利用缓存增量式的原理优化拓扑节点,进而构建精简式混合地图.实验结果表明,通过拓扑图构建策略的改进,所提出方法具有良好的导航性能.     

Mission-based online generation of probabilistic monitoring models for mobile robot navigation using Petri nets

This paper presents a generic hybrid monitoring approach, which allows the detection of inconsistencies in the navigation of autonomous mobile robots using online-generated models. A mission on the context of the navigation corresponds to an autonomous navigation from a start to an end mission point. The operator defines this mission by selecting a final goal point. Based on this selection the monitoring models for the current mission must be generated online. The originalities of this work are (i) the association of classic state estimation based on a particle filter with a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both pieces of information to distinguish between external noise influences, internal component faults and global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model (iii) the online generation of the corresponding monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modelling of both. To show the feasibility of the approach we apply it to an intelligent wheelchair (IWC) as a special type of autonomous mobile robot.     

室外自主移动机器人AMOR的导航技术

在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。     

一种基于双层插值的路径规划及跟踪算法

针对基于栅格地图的经典路径规划算法的不足,提出了一种双层插值的路径规划算法——T* 算法． 算法首先应用快速行军插值法生成目标波形图,波形图假设波以目标点为中心经四个邻节点向外扩散且沿波传播方 向栅格效用值单调递增;然后在生成的目标波形图上采用线性插值法,从机器人当前位置沿着波逆向搜索一条到达 目标点的平滑路径;得到规划的路径后,针对非完整约束的移动机器人,采用改进的虚拟车辆方法跟踪生成的路径． 实验验证了算法的有效性．     

Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

Integrating planning and control for single-bodied wheeled mobile robots

This paper presents an approach to couple path planning and control for mobile robot navigation in a hybrid control framework. We build upon an existing hybrid control approach called sequential composition, in which a set of feedback control policies are prescribed on well-defined domains contained in the robot’s free space. Each control policy drives the robot to a goal set, which lies in the domain of a subsequent policy. Control policies are deployed into the free state space so that when composed among one another, the overall action of the set of control policies drives the robot to perform a task, such as moving from a start to a goal location or patrolling a perimeter. A planner determines the sequence of control policies to be invoked. When control policies defined in this framework respect the low-level dynamics and kinematics of the system, this formal approach guarantees that high-level tasks are either accomplished by a given set of policies, or verifies that the tasks are not achievable with the given policies.     

An autonomous mobile robot for carrying food trays to the aged and disabled

Our research objective is to realize sensor-based navigation for car-like mobile robots. We adopt the generalized Voronoi graph (GVG) for the robot's local path and a map representation. It has the advantage to describe the mobile robot's path for sensor-based navigation from the point of view of completeness and safety. However, it is impossible to apply the path to car-like mobile robots directly, because the limitation of the minimum turning radius for a car-like robot may prevent it from following the GVG exactly. To solve this problem, we propose a local smooth path-planning algorithm for car-like mobile robots. Basically, an initial local path is generated by a conventional path-planning algorithm using GVG theory and it is modified smoothly by a Bezier curve to enable the car-like robots to follow it by maximizing our evaluation function. In this paper, we introduce a local smooth path-planning algorithm based on the GVG and explain the details of our evaluation function. Simulation and experimental results support the validity of the algorithm.     

基于激光雷达的室外移动机器人避障与导航新方法

提出了一种应用于室外移动机器人避障与导航的新方法——角度势场法．此方法将当前视场极坐标系的二维障碍物信息转换到一维的角度域内,综合评估视场内的障碍物在角度域内产生的阻力效应,以及目标点在角度域内产生的引力效应,计算得出当前目标角度及通行函数,确定移动机器人驾驶角和速度的控制输出,做到兼顾移动机器人的安全与向目标点的行进．此方法已应用于室外移动机器人THMR V．     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

A global motion planner that learns from experience for autonomous mobile robots

A new technique for enhancing global path planning for mobile robots working in partially known as indoor environments is presented in this paper. The method is based on a graph approach that adapts the cost of the paths by incorporating travelling time from real experiences. The approach uses periodical measurements of time and position reached by the robot while moving to the goal to modify the costs of the branches. Consequently, the search of a feasible path from a static global map in dynamic environments is more realistic than employing a distance metric. Our approach has been tested in simulation as well on an autonomous robot. Results from both simulation and real experiences are discussed.     

移动机器人基于近似Voronoi图的3-D环境建模方法

提出一种三维复杂环境下移动机器人的环境建模与分析方法。通过平滑滤波得到环境地形高度变化的轮廓基本特征,以一阶微分方法分析满足移动机器人运行的平坦性,建立投影平面上的可行区域图。应用改进的近似Voronoi边界网络构造方法得到可行区域的网络化结构模型。该方法能够以较少的网络节点反映移动机器人运行环境中可行区域的网络化结构,从而降低路径规划的计算复杂度。该模型方法体现了三维环境的地形轮廓特征,因此能够有助于导航中的规划与定位问题的解决。     

基于粒子群算法的移动机器人路径规划

提出一种分步路径规划方法，首先采用链接图建立机器人工作空间模型,用Dijkstra算法求得链接图 最短路径；然后用粒子群算法对此路径进行优化，得到全局最优路径.仿真结果表明:所提方法简便可行,能够满足 移动机器人导航的高实时性要求,是机器人路径规划的一个较好方案.􀁱     

Training of a leaning agent for navigation--inspired by brain-machine interface.

The design clue for the remote control of a mobile robot is inspired by the Talwar's brain-machine interface technology for remotely training and controlling rats. Our biologically inspired autonomous robot control consciousness-based architecture (CBA) is used for the remote control of a robot as a substitute for a rat. CBA is a developmental hierarchy model of the relationship between consciousness and behavior, including a training algorithm. This training algorithm computes a shortcut path to a goal using a cognitive map created based on behavior obstructions during a single successful trial. However, failures in reaching the goal due to errors of the vision and dead reckoning sensors require human intervention to improve autonomous navigation. A human operator remotely intervenes in autonomous behaviors in two ways: low-level intervention in reflexive actions and high-level ones in the cognitive map. Experiments are conducted to test CBA functions for intervention with a joystick for a Khepera robot navigating from the center of a square obstacle with an open side toward a goal. Their statistical results show that both human interventions, especially high-level ones, are effective in drastically improving the success rate of autonomous detours.     

Real-time path planning of autonomous robots in a two-dimensional unknown dynamic navigation environment

This paper deals with the real-time path planning of an autonomous mobile robot in two-dimensional, unknown, dynamic multiple robot navigation space. In particular, a collision-free navigation path planning strategy is presented in real time by using a heuristichuman like approach. The heuristic scheme used here is based on thetrial and error methodology with the attempt to minimize the cost of the navigation efforts, when time plays a significant role. Past built-up navigation experience and current extracted information from the surrounding environment are used for the detection of other moving objects (robots) in the same navigation environment. Moreover, the determination of asecure navigation path is supported by a set of generic traffic priority rules followed by the autonomous robots moving in the same environment. Simulated results for two moving objects in the same navigation space are also presented.