轮式移动机器人滑移量重建与滑移补偿控制

为解决轮式移动机器人的滑移补偿控制问题,首先推导出车体侧滑角的表达式,然后将时变侧滑角的重建问题转化为对地面特性参数的辨识问题.利用Luenberger观测器设计出自适应辨识律,并证明了当控制输入满足持续激励条件时,可以准确辨识出地面特性参数.基于链式系统模型设计出滑移补偿控制器,在滑移角精确已知的条件下,可以保证位置误差收敛,姿态误差有界.仿真结果表明,基于所设计的自适应辨识律,可以准确地重建出滑移角,提高滑移控制精度.     

Linear estimation of the physical odometric parameters for differential-drive mobile robots

In this paper a calibration technique aimed at identifying the odometric parameters of differential-drive mobile robots is proposed. The algorithm is based on two successive least-squares estimations based on the continuous-time kinematic equations of motion; the time-discretization error, thus, is avoided. The use of the least-squares technique is made possible by working on a linear mapping between the unknowns and the measurements and is not the result of a linearization. Another advantage of the proposed technique is that no specific path is required. The basic technique makes use of video-camera measurements and absolute position readings of the wheels’ encoders; the use of different sensors and measurements of the wheels velocities is also discussed. Experimental results with a mobile robot Khepera II confirm the effectiveness of the proposed technique.

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.     

Model-based sonar localisation for mobile robots

We describe a sonar localisation system for autonomous mobile robot navigation in a known environment, which tries to extract as much information as possible from the sensors by building a detailed probabilistic model of each sonar event. It takes account of multiple hypotheses about the source of each signal and uses a probabilistic sensor fusion technique to merge the results into a single location update. The system is designed to run under our decentralised, highly parallel vehicle architecture, and we discuss some of the implementation techniques required to achieve this. The results of some initial simulations are presented.     

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.     

On-line expectation-based novelty detection for mobile robots

This paper presents a recurrent neural network based novelty filter where a Scitos G5 mobile robot explored the environment and built dynamic models of observed sensory–motor values, then the acquired models of normality are used to predict the expected future values of sensory–motor inputs during patrol. Novelties could be detected whenever the prediction error between models-predicted values and actual observed values exceeded a local novelty threshold. The network is trained on-line; it grows by inserting new nodes when abnormal observation is perceived from the environment; and also shrinks when the learned information is not necessary anymore. In addition, the network is also capable of learning region-specific novelty thresholds on-line continuously.To evaluate the proposed algorithm, real-world robotic experiments were conducted by fusing sensory perceptions (vision and laser sensors) and the robot motor control outputs (translational and rotational velocities). Experimental results showed that all of the novelty cases were highlighted by the proposed algorithms and it produced reliable local novelty thresholds while the robot patrols in the noisy environment. The statistical analysis showed that there was a strong correlation between the novelty filter responses and the actual novelty status. Furthermore, the filter was also compared with another novelty filter and the results showed that the proposed system performed better novelty detection.     

A single visual-servo controller of mobile robots with super-twisting control

This paper presents a novel approach for image-based visual servoing, extending the existing works that use the trifocal tensor (TT) as source for image measurements. In the proposed approach, singularities typically encountered in this kind of methods are avoided. A formulation of the TT-based control problem with a virtual target resulting from the vertical translation of the real target allows us to design a single controller, able to regulate the robot pose towards the desired configuration, without local minima. In this context, we introduce a super-twisting control scheme guaranteeing continuous control inputs, while exhibiting strong robustness properties. Our approach is valid for perspective cameras as well as catadioptric systems obeying the central camera model. All these contributions are supported by convincing numerical simulations and experiments under a popular dynamic robot simulator.     

Corridor navigation and wall-following stable control for sonar-based mobile robots

In this paper, a mobile robot control law for corridor navigation and wall-following, based on sonar and odometric sensorial information is proposed. The control law allows for stable navigation avoiding actuator saturation. The posture information of the robot travelling through the corridor is estimated by using odometric and sonar sensing. The control system is theoretically proved to be asymptotically stable. Obstacle avoidance capability is added to the control system as a perturbation signal. A state variables estimation structure is proposed that fuses the sonar and odometric information. Experimental results are presented to show the performance of the proposed control system.     

Point stabilization of mobile robots via state-space exact feedback linearization

In this paper, we solve the point stabilization of mobile robots via state-space exact feedback linearization. The state-space exact feedback linearization has not been possible in the past for the point stabilization of mobile robots due to the restricted mobility caused by nonholonomic constraints. Under some proposed coordinates, however, the point stabilization problem can be exactly transformed into the problem of controlling a linear time-invariant system. Thus, using well-established linear control theory, the point stabilization of robots can be easily formulated.     

Optimal location of mouse sensors on mobile robots for position sensing

Optical mouse sensors have been utilized recently to measure the position and orientation of a mobile robot. This work provides a systematic solution to the problem of locating N optical mouse sensors on a mobile robot with the aim of increasing the quality of the position measurements. The developed analysis gives insights on how the selection of a particular configuration influences the estimation of the robot position, and it allows to compare the effectiveness of different configurations. The results are derived from the analysis of the singular values of a particular matrix obtained by solving the sensor kinematics problem. Moreover, given any mobile robot platform, an end-user procedure is provided to select the best location for N optical mouse sensors on such a platform. The procedure consists of solving a feasible constrained optimization problem.     

Potential field method to navigate several mobile robots

Navigation of mobile robots remains one of the most challenging functions to carry out. Potential Field Method (PFM) is rapidly gaining popularity in navigation and obstacle avoidance applications for mobile robots because of its elegance. Here a modified potential field method for robots navigation has been described. The developed potential field function takes care of both obstacles and targets. The final aim of the robots is to reach some pre-defined targets. The new potential function can configure a free space, which is free from any local minima irrespective of number of repulsive nodes (obstacles) in the configured space. There is a unique global minimum for an attractive node (target) whose region of attraction extends over the whole free space. Simulation results show that the proposed potential field method is suitable for navigation of several mobile robots in complex and unknown environments. Saroj Kumar Pradhan is faculty of Mechanical Engineering Department with N.I.T., Hamirpur, HP, India. He has received his B.E. degree in Mechanical Engineering from Utkal University and M.E. in Machine Design and Analysis from NIT Rourkela. He has published more than 17 technical papers in international journals and conference proceedings. His areas of research include mobile robots navigation and vibration of multilayred beams. Dayal R. Parhi is working as Assistant Professor at NIT Rourkela, India. He has obtained his first Ph.D. degree in “Mobile Robotics” from United Kingdom and Second Ph.D. in “Mechanical Vibration” from India. He has visited CMU, USA as a “Visiting Scientist” in the field of “Mobile Robotics”. His main areas of current research are “Robotics” and “Mechanical Vibration”. He is supervising five Ph.D. students in the fields of Robotics and Vibration. Email: dayalparhi@yahoo.com. Anup Kumar Panda Received his M.Tech degree from IIT, Kharagpur in 1993 and Ph.D. degree from Utkal University in 2001. He is currently an assistant professor in the Department of Electrical Engineering at National Institute of Technology, Rourkela, India. His areas of research include robotics, Machine Drives, harmonics and power quality. He has published more than 30 technical papers in journals and conference proceedings. He is now involved in two R&D projects funded by Government of India. R. K. Behera is a Senior Lecturer of Mechanical Engineering at National Institute of Technology, Rourkela, India. He has been working as lecturer for more than 10 years. He obtained his BE degree from IGIT, Sarang, of Utkal University. He obtained his ME and Ph.D degrees, both in the field of mechanical engineering from NIT Rourkela.     

A hybrid control approach to action coordination for mobile robots

In this paper, the problem concerning how to coordinate the contributions from concurrent controllers, when controlling mobile robots, is investigated. It is shown how a behavior based control system for autonomous robots can be modeled as a hybrid automaton, where each node corresponds to a distinct robot behavior. This type of construction gives rise to chattering executions, but it is shown how regularized automata can be used to solve this problem. As an illustration, the obstacle-negotiation problem is solved by using a combination of a robust path-following behavior and a reactive obstacle-avoidance behavior that move the robot around a given obstacle at a predefined safety distance.     

Oracle-based flocking of mobile robots in crash-recovery model

This paper considers a system of autonomous mobile robots that can move freely in a two-dimensional plane, and where a number of them can fail by crashing. The crash of a robot can be either permanent or temporary, that is, after its crash the robot either executes no action or it recovers from its failure. These robots repeatedly go through a succession of activation cycles during which they observe the environment, compute a destination and move. In particular, we assume weak robots, in the sense that robots cannot communicate explicitly between each other. Also, they cannot remember their past computations (i.e., they are oblivious). Finally, robots do not agree on a common coordinate system.In this paper, we address the fault-tolerant flocking problem under the crash-recovery model. That is, starting from any initial configuration, a group of non-faulty robots are required to form a desired pattern, and move together while following a robot leader moving on some trajectory, and keeping such a pattern in movement. Specifically, we propose a fault-tolerant flocking algorithm in the semi-synchronous model that allows correct robots to dynamically form a regular polygon in finite time, and maintain it in movement infinitely often. Our algorithm relies on the existence of two devices, namely an eventually perfect failure detector device to ensure failure detection, and also an eventual leader device to handle leader election. The algorithm tolerates permanent crash failures, and also crash-recovery failures of robots due to its oblivious feature. The proposed algorithm ensures the necessary restrictions on the movement of robots in order to avoid collisions between them. In addition, it is robust with respect to changes in the environment.     

Evolution of neural control structures: some experiments on mobile robots

From perception to action and from action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness or incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is a very important limitation of the evolutionary approach.     

Vision-based formation control of mobile robots

1Introduction Formation control of multiple vehicles,such ascooperative control of a group of mobile robots[1~4]and multiple spacecraft[5,6],has beenrecognized as a keytechnologyfor the future and studied by many researchersin recent years.The various control approaches to multiplevehicle formation reported in the literature can becategorized into three groups:leader following schemes;behavior_based methods;and virtual structure techniques.In the leader following approach[1,2,7],one of thevehi…     

A parallel processing architecture for sensor-based control of intelligent mobile robots

Parallel processing plays an important role in sensor-based control of intelligent mobile robots. This paper describes the design and implementation of a parallel processing architecture used for real-time, sensor-based control of mobile robots. This architecture takes the form of a network of sensing and control nodes, based on a novel module that we call Locally Intelligent Control Agent (LICA). It is a hybrid control architecture containing low-level feedback control loops and high-level decision making components. All the sensing, planning, and control tasks for intelligent control of a mobile robot are distributed across such a network, and operate in parallel. It has been used successfully in many experiments to perform planning and navigation tasks in real-time. Such a generic architecture can be readily applied to many diverse applications.     

Vision-based formation control of mobile robots

In this paper,a formatio n control algorithm and an obstacle avoidance control algorithm for mobile robots are developed based on a relative motion sensory system such as a pan/tilt camera vision system,without the need for global sensing and communication between robots.This is achieved by e mploying the velocity variation,instead of actual ve locities,as the control inputs.Simulation and experi mental results have demonstrated the effectiveness of the proposed control methods.     

Optimal and suboptimal motion planning for collision avoidance of mobile robots in non-stationary environments

An optimal control formulation of the problem of collision avoidance of mobile robots moving in terrains containingmoving obstacles is presented. A dynamic model of the mobile robot and the dynamic constraints are derived. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Furthermore, time consistency with the nominal plan is desirable. Two solutions are obtained: (1) A numerical solution of the optimization problem and a perturbation type of control to update the optimal plan and (2) A computationally efficient method giving near optimal solutions. Simulation results verify the value of the proposed strategies and allow for comparisons.     

基于深度信息的移动机器人室内环境三维地图创建

针对使用扩展卡尔曼滤波(EKF）进行环境地图的创建对线性系统效果较好而对非线性系统的线性化受误差影响较大的问题,提出一种基于对Kinect采集到的环境数据和迭代扩展卡尔曼滤波(IEKF）算法的室内环境三维地图创建。该方法使用成本较低的Kinect传感器获取深度数据然后结合IEKF实现摄像头轨迹预测,最后利用最近点迭代(ICP）算法对深度图像进行配准得到室内环境三维点云图。实验结果表明,IEKF算法与传统的EKF算法相比,得到的轨迹更平滑、误差更小,同时所得到的三维点云图更加光滑。该方法实现了三维地图构建,较为实用,效果较好。     

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.