非完整移动机器人全局路径跟踪控制

根据制导路径跟踪理论,提出了一种非完整移动机器人全局路径跟踪控制方法.这一方法首先在路径坐标系上计算实际位置与期望位置的误差,利用制导的路径跟踪理论,导出消除该误差所需的姿态角和路径参数更新律,然后据此求解角速度及实际控制.文中还给出了初始路径参考点的计算方法,分析了路径跟踪方向和反转方法.稳定性分析证明该方法没有控制奇异点,受控闭环系统全局一致渐近稳定.最后通过移动机器人典型路径跟踪实验验证了所提出方法的可行性.     

Global path planning of mobile robots using a memetic algorithm

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.     

基于路径跟踪控制方法的拖挂式机器人系统路径规划算法

为解决拖挂式移动机器人系统路径规划算法精准性低、稳定性差和无法考虑系统间安全性等的问题,提出一种基于路径跟踪方法的路径规划算法。该算法融合快速拓展随机树（RRT）基本算法和路径跟踪控制方程,通过自动拟合样条曲线,跟踪并生成节点间轨迹,以此提高路径精准性;加入系统夹角约束条件和节点击中机制提高算法稳定性和结果安全性;此外,加入贪心优化算法,针对结果路径进行优化处理。通过仿真实验结果表明,相较基本RRT算法,改进算法搜索得到的路径更贴近实际运动轨迹,在安全性和成功率上优于原算法,能够满足快速设计或实时系统的需求。     

A visual path-following learning approach for industrial robots using DRL

Manufacturing companies are in constant need for improved agility. An adequate combination of speed, responsiveness, and business agility to cope with fluctuating raw material costs is essential for today’s increasingly demanding markets. Agility in robots is key in operations requiring on-demand control of a robot’s tool position and orientation, reducing or eliminating extra programming efforts. Vision-based perception using full-state or partial-state observations and learning techniques are useful to create truly adaptive industrial robots. We propose using a Deep Reinforcement Learning (DRL) approach to solve path-following tasks using a simplified virtual environment with domain randomisation to provide the agent with enough exploration and observation variability during the training to generate useful policies to be transferred to an industrial robot. We validated our approach using a KUKA KR16HW robot equipped with a Fronius GMAW welding machine. The path was manually drawn on two workpieces so the robot was able to perceive, learn and follow it during welding experiments. It was also found that small processing times due to motion prediction (3.5 ms) did not slow down the process, which resulted in smooth robot operations. The novel approach can be implemented onto different industrial robots to carry out different tasks requiring material deposition.     

Active Markov localization for mobile robots

Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot's effectors during localization. This paper proposes an active localization approach. The approach is based on Markov localization and provides rational criteria for (1) setting the robot's motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximative world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.     

Hybrid power supply for mobile robots

Service robots are frequently fitted with end-effecters, which require high energy, to achieve a specific task. Robots require high-power energy sources that can meet several goals simultaneously. Given the technology of today, a battery-driven robot cannot work for long on a single charge. Todevelop a new powerful battery technology may take many years of exhaustive research. In this paper, we present a dual-energized scheme called HYbrid POwer Supply (HYPOS) that integrates the function of batteries and a household electric system to supply a robot. This technique can be realized by existing technology and is applicable to a variety of indoor applications. The challenge of implementing the HYPOS system is to develop the outlet-connection procedures and to handle the electric cord to reduce movement disturbance. We report on the prototype robot and the experiments that illustrate the effectiveness and practicality of this proposed system. The result has shown that the HYPOS system can supply a robot consecutively for almost 2 h and its efficiency is 73.6%, while the efficiency of a lead-acid battery is only 38.5%.     

A membrane computing framework for self-reconfigurable robots

Self-reconfigurable robots are built by modules which can move in relationship to each other, which allows the robot to change its physical form. Finding a sequence of module moves that reconfigures the robot from the initial configuration to the goal configuration is a hard task and many control algorithms have been proposed. In this paper, we present a novel method which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. This new approach has been tested with computer simulations and real-world experiments performed with modular self-reconfigurable robots and represents a new point of view with respect other control methods found in the literature.

     

Embodied categorisation for vision-guided mobile robots

This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be effective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate efficient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more efficient and robust.     

Vision-guided mobile robots for design competitions

We present the Teaching system integration in engineering curricula at universities via popular and effective robot-design competitions. In this article, we first present the robot kit we use and discuss the experiences we gained in, and shortcomings of, our robot competitions. We then present the low-cost color vision system, developed especially for our course. We also discuss our experiences in the 1998 competition, where we first made use of the system, and the 2001 competition. Finally, we present the results of a questionnaire given to students who have completed the course.     

Dynamic omnidirectional vision for mobile robots

Mobile robotic devices hold great promise for a variety of applications in industry. A key step in the design of a mobile robot is to determine the navigation method for mobility control. The purpose of this paper is to describe a new algorithm for omnidirectional vision navigation. A prototype omnidirectional vision system and the implementation of the navigation techniques using this modern sensor and an advanced automatic image processor is described. The significance of this work is in the development of a new and novel approach—dynamic omnidirectional vision for mobile robots and autonomous guided vehicles.     

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.     

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.     

Local map-based exploration for mobile robots

For an accurate and efficient exploration, a local map-based exploration strategy is proposed. Segmented frontiers and relative transformations constitute a tree structure; using frontier segmentation and a local map management method, a robot can expand the mapped environment by moving along the tree structure. Although this local map-based exploration method uses only local maps and adjacent node information, mapping completion and efficiency can be greatly improved by merging and updating the frontier nodes. Simulation results demonstrate that the computational time does not increase during the exploration process, or when the resulting map becomes large. Additionally, the resulting path is effective in reducing the uncertainty in simultaneous localization and mapping or localization because of the loop-inducing characteristics from the child node to the parent node.     

Sensor-based self-localization for wheeled mobile robots

In this article, we demonstrate a reliable, robust, and computationally efficient algorithm that uses inexpensive hardware to localize a mobile robot in a rather structured environment that is relatively consistent to an a priori map. Furthermore, the incorporation of thresholding makes possible the localization of the robot even in the presence of objects not depicted in the a priori map. An Extended Kalman Filter is used to combine dead-reckoning, ultrasonic, and infrared sensor data to estimate current position and orientation. Implementation issues and experimental results from experience with a mobile robot, Nomad 200, are also presented. © 1995 John Wiley & Sons, Inc.     

Holonomy in mobile robots

The search for a simple and accurate odometry is a main concern when working with mobile robots. This article presents a general analysis of the problem and proposes a particular solution to improve the odometry. The three crucial kinematical aspects of mobile robots (mobility, control, and positioning) are reviewed in detail for vehicles based both in conventional and in omnidirectional wheels. The latter case is more suitable from a maneuvering point of view as it provides the robot frame with the three Degrees Of Freedom (DOF) of plane motion without singular configurations. Moreover, a suitable design of the omnidirectional wheels leads to a strictly invariant Jacobian matrix and thus to a linear control equation with constant coefficients. It is shown that such vehicles may have a holonomic behavior when moving under suitable kinematical restrictions without constraining their trajectory. In that case, the odometry is algebraic (instead of integrative) and thus more accurate. An application case is presented.     

Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.     

Development of membrane controllers for mobile robots

The main contribution of this paper is the introduction of the new concept of membrane controller based on the structure and functioning of a deterministic numerical P system. The procedure for developing a membrane controller and for using it to control a mobile robot is explained and several test cases are given in which membrane controllers are used to control both simulated and real mobile robots and to generate various desired behaviours (obstacle avoidance, wall following, and follow the leader). The experiments reported in this paper validate the concept and prove that the performance of a membrane controller is comparable to or better than that of other controllers (such as fuzzy logic controllers).     

An appearance-based visual compass for mobile robots

Localization is one of the most important basic skills of a mobile robot. Most approaches, however, still rely either on special sensors or require artificial environments. In this article, a novel approach is presented that can provide compass information for localization, purely based on the visual appearance of a room. A robot using such a visual compass can quickly learn a cylindrical map of the environment, consisting of simple statistical features that can be computed very quickly. The visual compass algorithm is efficient, scalable and can therefore be used in real-time on almost any contemporary robotic platform. Extensive experiments on a Sony Aibo robot have validated that the approach works in a vast variety of environments.     

Motion planning for formations of mobile robots

This paper is concerned with planning the motion of mobile robots in formation, which means certain geometrical constraints are imposed on the relative positions and orientations of the robots throughout their travel. Specifically, a method of planning motion for formations of mobile robots with non-holonomic constraints is presented. The kinematic equations developed allow a certain class of formations to be maintained while the group as a whole exhibits motion. The work was validated using the Stanford Micro-Autonomous RoverS Testbed.