Distributed algorithms for partitioning a swarm of autonomous mobile robots

A number of recent studies address systems of mobile autonomous robots from a distributed computing point of view. Although such systems employ robots that are relatively weak and simple (i.e., dimensionless, oblivious and anonymous), they are nevertheless expected to have strong fault tolerance capabilities as a group. This paper studies the partitioning problem, where n$n$ robots must divide themselves into k$k$ size-balanced groups, and examines the impact of common orientation on the solvability of this problem. First, deterministic crash-fault-tolerant algorithms are given for the problem in the asynchronous full-compass and semi-synchronous half-compass models, and a randomized algorithm is given for the semi-synchronous no-compass model. Next, the role of common orientation shared by the robots is examined. Necessary and sufficient conditions for the partitioning problem to be solvable are given in the different timing models. Finally, the problem is proved to be unsolvable in the no-compass synchronous model.     

Models of computation for reactive control of autonomous mobile robots

The paper studies computation models for tasks performed by autonomous mobile robots. Such tasks can be accomplished by reactive control algorithms. Reactive control systems can be described using different models of computation which have as distinguishing feature the abstraction level of time. Thus, three computation models are defined: the untimed model, the synchronous model and the timed model. It is shown that the clocked-synchronous model of computation is more appropriate for describing the controller for a parallel parking task.     

Planetary exploration by a mobile robot: Mission teleprogramming and autonomous navigation

Sending mobile robots to accomplish planet exploration missions is scientifically promising and technologically challenging. We present in this paper a complete approach that encompasses the major aspects involved in the design of a robotic system for planetary exploration. It includes mission teleprogramming and supervision at a ground station, and autonomous mission execution by the remote mobile robot. We have partially implemented and validated these concepts. Experimental results illustrate the approach and the results.     

A hybrid navigation strategy for multiple mobile robots

A hybrid navigation strategy is proposed in this paper for solving the navigation problem of multiple mobile robots. The proposed strategy integrates three algorithms that represent three different types of existing methods in a layered system. The bottom-up architecture of this system is the main contribution of this paper. This architecture pursues reliable low-level layers that can independently work in as much cases as possible, and the high-level layer is used only when it is necessary for guaranteeing convergence in complex situations. The simulation results show that the proposed strategy has well combined the algorithms of different types from the perspective of pursuing reactivity in the premise of ensuring convergence. Compared with the traditional top-down hybrid architecture, the bottom-up architecture proposed in this paper is more suitable for multi-robot navigation since it can better utilize the advantages of different algorithms to deal with different situations. The experiments on real robots have further verified the applicability of the proposed strategy.     

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。     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

Deliberation and reactivity in autonomous mobile robots

This paper discusses issues related to the design of the control architectures for an autonomous mobile robot capable of performing tasks efficiently and intelligently, i.e. in a manner adapted to its environment, to its own state and to the execution status of its task. We present our developments and experimentations on mobile robot navigation and show how it is necessary to produce representations at several levels of abstraction, that are used by adequate processes for obstacle detection, target recognition, robot localization, and motion planning and control. We also show that deliberation is necessary for the robot in order to anticipate events, take efficient decisions, and react adequately to asynchronous events. We also discuss the organization of the system, i.e. the design of the control architecture.     

多移动机器人系统运动协调研究综述

运动协调是多移动机器人系统领域的主要研究热点之一。在阐述多机器人合作与运动协调两者关系的基础上,给出了多机器人系统运动协调的问题描述及其分类;从主要研究方法的角度,归纳总结了多机器人系统运动协调的国内外研究动态。最后,对运动协调在多移动机器人系统领域的前景和研究方向作出了展望。     

Moving obstacle detection and recognition by optical flow pattern analysis for mobile robots

_—This paper presents a new idea for an obstacle recognition method for mobile robots by analyzing optical flow information acquired from dynamic images. First, the optical flow field is detected in image sequences from a camera on a moving observer and moving object candidates are extracted by using a normalized square residual error [focus of expansion (FOE) residual error] value that is calculated in the process of estimating the FOE. Next, the optical flow directions and intensity values are stored for the pixels involved in each candidate region to calculate the distribution width values around the principal axes of inertia and the direction of the principal axes. Finally, each candidate is classified into an object category that is expected to appear in the scene by comparing the proportion and the direction values with standard data ranges for the objects which are determined by preliminary experiments. Experimental results of car/bicycle/pedestrian recognition in real outdoor scenes have shown the effectiveness of the proposed method.     

异质多移动机器人协同技术研究的进展

随着移动机器人应用的领域和范围的不断扩展，多移动机器人由于其单个机器人无法比拟的优越性已经越来越受到重视．从体系结构、协作与协调、协作环境感知与定位、重构及机器学习几个重要课题对多移动机器人协同技术进行了综述，尤其侧重于各种技术如何处理和包容团队中的异质性，并分析了本领域中的研究难点问题，最后展望了异质多移动机器人研究的前景与发展趋势．     

移动机器人步长调整算法的S函数应用研究

机器人在对于一些气味源、声源、平衡位置等目标搜索时,为了精确定位,一般采用变步长的算法。根据S函数,设计出了一种新型步长调整算法。与传统的最陡下降法相比,其步长调整速度快,适应外部环境能力强,并且包含了方向信息。实验证明该方法是一种有效的移动机器人步长调整算法。     

Switching control approach for stable navigation of mobile robots in unknown environments

This paper presents a stable switching control strategy for the parking problem of non-holonomic mobile robots. First, it is proposed a positioning-orientation switching controller for the parking problem. With this strategy robot backwards motions are avoided and the robot heading is always in the direction of the goal point facilitating the obstacle handling. Second, the avoidance of unexpected obstacles is considered in a reactive way by following the contour of the obstacles. Next, the stability of the switching parking/obstacle-avoider controller is analyzed showing stability under reasonable conditions. Finally, the good performance and the feasibility of this approach are shown through several experimental results.     

基于行为动力学方法的移动机器人轨迹追踪

研究了基于行为动力学方法的移动机器人轨迹追踪。在总结行为动力学理论的基础上,根据轨迹追踪任务要求,确定航向角和速度作为行为变量,同时构建了接近吸引子动力学方程,并在考虑机器人与路径期望点之间距离这一间接耦合参数基础上,建立了速度动力学方程,并分析了该动力系统的收敛性。最后的仿真结果表明该方法正确、可行,且机器人能有效地完成追踪任务。     

Fault-tolerant flocking for a group of autonomous mobile robots

Consider a system composed of mobile robots that move on the plane, each of which independently executing its own instance of an algorithm. Given a desired geometric pattern, the flocking problem consists in ensuring that the robots form this pattern and maintain it while moving together on the plane. In this paper, we explore flocking in the presence of faulty robots, where the desired pattern is a regular polygon. We propose a distributed fault tolerant flocking algorithm assuming a semi-synchronous model with a k-bounded scheduler, in the sense that no robot is activated no more than k times between any two consecutive activations of any other robot.The algorithm is composed of three parts: failure detector, ranking assignment, and flocking algorithm. The role of the rank assignment is to provide a persistent and unique ranking for the robots. The failure detector identifies the set of currently correct robots in the system. Finally, the flocking algorithm handles the movement and reconfiguration of the flock, while maintaining the desired shape. The difficulty of the problem comes from the combination of the three parts, together with the necessity to prevent collisions and allow the rotation of the flock. We formally prove the correctness of our proposed solution.     

High-speed laser localization for mobile robots

This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closed-form solution for the matching of two laser scans, the use of natural scan features and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.     

基于传感器信息的智能移动机器人导航评述

导航是研究智能移动机器人技术中的一个重要领域,对自主导航技术的关键问题——路径规划进行了评述。路径规划一般可分为基于模型的环境信息完全知道的全局路径规划和基于传感器的环境信息完全未知或部分未知的局部路径规划2种类型。分别对各种方法的发展现状进行了总结,指出了各种方法的优点和不足。     

一种引入通信的多移动机器人编队方法*

提出以视觉跟踪为基础并引入通信进行多机器人的编队控制方法,根据需要编写了一种新的通信协议,采用闭环l-Φ实现编队算法.这种多机器人编队控制避免了视觉系统的局限,能够更好地在复杂未知环境中协作完成任务,解决了编队控制的无反馈和实时性不高的问题,使得机器人能够准确迅速地进行跟踪和通信编队,一起顺利达到目标点.试验结果证明了该方法的有效性.     

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.     

Emerging motor behaviors: Learning joint coordination in articulated mobile robots

In this paper, we analyze the insights behind the common approach to the assessment of robot motor behaviors in articulated mobile structures with compromised dynamic balance. We present a new approach to this problem and a methodology that implements it for motor behaviors encapsulated in rest-to-rest motions. As well as common methods, we assume the availability of kinematic information about the solution to the task, but reference is not made to the workspace, allowing the workspace to be free of restrictions. Our control framework, based on local control policies at the joint acceleration level, attracts actuated degrees of freedom (DOFs) to the desired final configuration; meanwhile, the resulting final states of the unactuated DOFs are viewed as an indirect consequence of the profile of the policies. Dynamical systems are used as acceleration policies, providing the actuated system with convenient attractor properties. The control policies, parameterized around imposed simple primitives, are deformed by means of changes in the parameters. This modulation is optimized, by means of a stochastic algorithm, in order to control the unactuated DOFs and thus carry out the desired motor behavior.     

Robust formation tracking control of mobile robots via one-to-one time-varying communication

We solve the formation tracking control problem for mobile robots via linear control, under the assumption that each agent communicates only with one ‘leader’ robot and with one follower, hence forming a spanning-tree topology. We assume that the communication may be interrupted on intervals of time. As in the classical tracking control problem for non-holonomic systems, the swarm is driven by a fictitious robot which moves about freely and which is a leader to one robot only. Our control approach is decentralised and the control laws are linear with time-varying gains; in particular, this accounts for the case when position measurements may be lost over intervals of time. For both velocity-controlled and force-controlled systems, we establish uniform global exponential stability, hence consensus formation tracking, for the error system under a condition of persistency of excitation on the reference angular velocity of the virtual leader and on the control gains.