Navigation of Mobile Robots Using a Fuzzy Logic Controller

This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.     

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.     

一种多模式全向移动机器人攀爬楼梯的步态

针对移动机器人在户外运动中所遇到的台阶、楼梯等复杂地形,设计了一种可攀爬楼梯的多模式全向移动机器人。通过切换运动模态,该机器人既能像传统移动机器人一样快速移动,又具备了足式机器人的越障能力。首先,分析并构建了多模式全向移动机器人的运动学模型;其次,研究了该机器人越障能力和质心位置之间的关系并计算了该机器人可以翻越台阶的...     

Kinematic modeling of mobile robots by transfer method of augmented generalized coordinates

A kinematic modeling method, which is directly applicable to any type of planar mobile robots, is proposed in this work. Since holonomic constraints have the same differential form as nonholonomic constraints, the instantaneous motion of the mobile robot at current configuration can be modeled as that of a parallel manipulator. A pseudo joint model denoting the interface between the wheel and the ground (i.e., the position of base of the mobile robot) enables the derivation of this equivalent kinematic model. The instantaneous kinematic structures of four different wheels are modeled as multiple pseudo joints. Then, the transfer method of augmented generalized coordinates, which has been popularly employed in modeling of parallel manipulators, is applied to obtain the instantaneous kinematic models of mobile robots. The kinematic models of six different types of planar mobile robots are derived to show the effectiveness of the proposed modeling method. Lastly, for the mobile robot equipped with four conventional wheels, an algorithm estimating a sensed forward solution for the given information of the rotational velocities of the four wheels is discussed. © 2004 Wiley Periodicals, Inc.     

Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.     

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.     

Dynamics,control, and simulation of robots with differential drive

The problem of synthesizing trajectories, planning and implementing the motion of a mobile robot is investigated. The statement of the problem is motivated by the need for designing an optimal control of robots with two wheels controlled independently. Mobile robots with this kinematic scheme are called robots with “differential drive.”     

Task-based Hardware Reconfiguration in Mobile Robots Using FPGAs

This paper presents a methodology for the realization of intelligent, task-based reconfiguration of the computational hardware for mobile robot applications. Task requirements are first partitioned into requirements on the system hardware and software. Architecture is proposed that enables these requirements to be addressed through appropriate hardware and software components. Hardware–software co-design and hardware reconfiguration are utilized to design robotic systems that are fault-tolerant and have improved reliability. It is shown that this design enables the implementation of efficient controllers for each task of the robot thereby permitting better operational efficiency using fixed computational resources. The approach is validated through case studies where a team of robots is configured and the behavior of the robots is dynamically modified at run-time. It is demonstrated through this implementation that the design procedure results in increased flexibility in configuration at run-time. The ability to reconfigure the resources also aids collaboration between robots, and results in improved performance and fault tolerance.     

Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of optimal assignment algorithms, and we expect this to be a fruitful area for future research.     

一种改进多机器人分布式滚动路径规划算法

针对多移动机器人全局静态环境未知的路径规划问题,采用了一个全局性能指标,在保证路径较优的情况下,最小化机器人的停顿时间,提出机器人之间以修正局部路径为主的协调策略。根据多机器人滚动路径算法的原理,设计了改进的多机器人分布式滚动路径规划算法。在已有仿真系统上进行测试,比较了所提出的协调策略与改变机器人移动速度协调策略对性能指标的影响。仿真结果表明,静态环境未知情况下,机器人可以并行规划各自的协调路径。     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Design and implementation of a novel hybrid quadruped spherical mobile robot

Spherical mobile robots are a novel type of mobile robots having some advantages in motion over other ordinary mobile robots. The advantages can be related to their symmetric spherical shape. Despite many works being conducted in recent years on spherical mobile robots, it seems that finding the best driving mechanism with higher efficiency still needs much research. In this article, a novel type of spherical mobile robot is introduced. This robot has a hybrid structure of the spherical robots and ordinary four legged or quadruped robots. Adding legs to the spherical robot reduces some disadvantages of its behavior. After introduction of the mentioned robot, its dynamic model based on Lagrange equations is obtained. The accuracy of the developed dynamic model in tracking a trajectory is verified through a dynamic simulation. Experimental results in tracking a square trajectory is presented to show the verification.     

Optimal cooperative collision avoidance between multiple robots based on Bernstein–Bézier curves

In this paper a new cooperative collision-avoidance method for multiple, nonholonomic robots based on Bernstein–Bézier curves is presented. The main contribution focuses on an optimal, cooperative, collision avoidance for a multi-robot system where the velocities and accelerations of the mobile robots are constrained and the start and the goal velocity are defined for each robot. The optimal path of each robot, from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the path lengths subjected to the distances between the robots, which should be larger than the minimum distance defined as the safety distance, and subjected to the velocities and accelerations, which should be lower than the maximum allowed for each robot. The model-predictive trajectory tracking is used to drive the robots on the obtained reference paths. The results of the path planning, real experiments and some future work ideas are discussed.     

Stair Climber Smart Mobile Robot (MSRox)

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.     

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。     

基于粒子群算法的移动机器人全局最优路径规划*

以栅格法和粒子群算法为基础,提出了一种新的机器人实时全局最优路径规划方法.该方法包括采用栅格法对环境进行建模和直接运用粒子群算法在环境模型中搜索全局最优路径.在计算机上进行了仿真,仿真结果证明了该方法的可行性和有效性.     

室外未知环境下的AGV地貌主动探索感知

智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。     

Navigation of mobile robot by using D++ algorithm

The navigation of mobile robots is a vital aspect of technology in robotics. We applied the D++ algorithm, which is a novel and improved path-planning algorithm, to the navigation of mobile robots. The D++ algorithm combines Dijkstra??s algorithm with the idea of a sensor-based method, such that Dijkstra??s algorithm is adapted to local search, and the robot can determine its next move in real-time. Although the D++ algorithm frequently runs local search with limited ranges, it can compute optimum paths by expanding the size of the searching range to avoid local minima. In addition, we verified the performance of the D++ algorithm by applying it to a real robot in a number of environments. The use of the D++ algorithm enables robots to navigate efficiently in unknown, large, complex and dynamic environments.     

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.