Navigation strategies for multiple autonomous mobile robots moving in formation

The problem of deriving navigation strategies for a fleet of autonomous mobile robots moving in formation is considered. Here each robot is represented by a particle with a spherical effective spatial domain and a specified cone of visibility. The global motion of each robot in the world space is described by the equations of motion of the robot's center of mass. First, methods for formation generation are discussed. Then, simple navigation strategies for robots moving in formation are derived. A sufficient condition for the stability of a desired formation pattern for a fleet of robots each equipped with the navigation strategy based on nearest neighbor tracking is developed. The dynamic behavior of robot fleets consisting of three or more robots moving in formation in a plane is studied by means of computer simulation.     

基于行为的多机器人任意队形的控制

针对多机器人队形优化控制任务,提出一种快速收敛的机器人任意队形的控制算法。各机器人在奔向目标的过程中以队形的几何中心为参考点,自主地确定队形向量。在保持队形的过程中,采用动态死区法,通过对各个区域大小的控制达到对机器人速度的控制,维持规定队形。采用反向避碰、切线避障,根据各机器人间的位置,引入整体队形向量约束机器人的方向,达到机器人整体队形的方向与机器人运动方向一致。实验结果表明该算法可以快速、有效地完成各种编队任务。     

全自主机器人足球系统的全局地图构建研究

研究和讨论了如何通过多机器人的协作,实现全局地图的构建．在单个机器人通过自身携带的多传感器进行局部地图构建的基础上,研究了前向单目视觉传感器的建模方法,在此观测模型的基础上,用极大似然融合算法对球的位置信息进行融合,而对于多机器人返回的对方机器人位置信息,使用基于密度的空间聚类算法（DBSCAN）进行信息融合,从而实现全局地图构建．实验结果表明,通过多机器人的协作,可以准确地构建出全局地图,弥补了单个机器人自身传感器的有限感知范围,本文研究的方法完全满足全自主机器人足球比赛中动态环境地图构建的需要．     

Asynchronous deterministic rendezvous in bounded terrains

Two mobile agents (robots) have to meet in an a priori unknown bounded terrain modeled as a polygon, possibly with polygonal obstacles. Robots are modeled as points, and each of them is equipped with a compass. Compasses of robots may be incoherent. Robots construct their routes, but the actual walk of each robot is decided by the adversary that may, e.g., speed up or slow down the robot. We consider several scenarios, depending on three factors: (1) obstacles in the terrain are present, or not, (2) compasses of both robots agree, or not, (3) robots have or do not have a map of the terrain with their positions marked. The cost of a rendezvous algorithm is the worst-case sum of lengths of the robots’ trajectories until they meet. For each scenario, we design a deterministic rendezvous algorithm and analyze its cost. We also prove lower bounds on the cost of any deterministic rendezvous algorithm in each case. For all scenarios these bounds are tight.     

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.     

快速收敛的机器人部队任意队形分布式控制算法

针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队形和队形向量不易确定的问题 ,通过由各智能体自主简单的确定自己的队形向量 ,从理论上扩展基于队形向量的队形控制原理以生成任意队形 ,改进机器人的运动方式以提高收敛速度 ,提出一种快速收敛的机器人部队任意队形分布式控制算法 .仿真结果表明 ,该算法可以形成任意队形 ,比现有控制算法的收敛速度快 ,队形收敛所需的时间仅为现有算法的 10 %左右     

Am I acceptable to you? Effect of a robot’s verbal language forms on people’s social distance from robots

This study is to examine the effect of robots’ language forms on people’s acceptance of robots. We applied a concept of social distance to measure people’s acceptance of robots. In an experiment, calling participants by name vs. not calling by name as well as the robot’s speech styles (familiar vs. honorific), were used to impose a verticality and horizontality of social relationships between participants and robots. After the conversation with a robot, participants rated the robot’s interpersonal traits and their comfortable approach distance to the robot, and their response to the robot during the experiment were analyzed. As a result, participants whom the robot called by their name perceived the robot as friendlier. They introduced themselves more actively, and were more intently focused on what the robot said. They asked the robot questions more frequently. Participants called by their names consequently approached the robot more closely than participants who were not called. An interaction effect was found between speech styles and whether names were used in regard to the perceived friendliness of robots, negative response to robots, and comfortable approach distance to robots. We discuss verbal interaction design for increasing people’s acceptance of robots.     

Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.     

Multi-observation sensor resetting localization with ambiguous landmarks

Successful approaches to the robot localization problem include particle filters, which estimate non-parametric localization belief distributions. Particle filters are successful at tracking a robot’s pose, although they fare poorly at determining the robot’s global pose. The global localization problem has been addressed for robots that sense unambiguous visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. Unfortunately, for robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new pose hypotheses across a wide region, in poses that may be inconsistent with previous observations. We introduce multi-observation sensor resetting (MOSR) to address the localization problem with sparse, ambiguous and noisy observations. MOSR merges observations from multiple frames to generate new hypotheses more effectively. We demonstrate experimentally on the NAO humanoid robots that MOSR converges more efficiently to the robot’s true pose than standard sensor resetting, and is more robust to systematic vision errors.     

A collaborative localization tolerant to recognition error by double-check particle exchange

Statistical algorithms using particle filters for collaborative multi-robot localization have been proposed. In these algorithms, by synchronizing every robot’s belief or exchanging particles of the robots with each other, fast and accurate localization is attained. These algorithms assume correct recognition of other robots, and the effects of recognition errors are not discussed. However, if the recognition of other robots is incorrect, a large amount of error in localization can occur. This article describes this problem. Furthermore, an algorithm for collaborative multi-robot localization is proposed in order to cope with this problem. In the proposed algorithm, the particles of a robot are sent to other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Particles received from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method is tolerant to recognition error by the remaining particles and evaluating the exchanged particles in the sending and receiving robots twice, and if there is no recognition error, the proposed method increases the accuracy of the estimation by these two evaluations. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.     

Interaction dynamics of multiple mobile robots with simple navigation strategies

The global dynamic behavior of multiple interacting autonomous mobile robots with simple navigation strategies is studied. Here, the effective spatial domain of each robot is taken to be a closed ball about its mass center. It is assumed that each robot has a specified cone of visibility such that interaction with other robots takes place only when they enter its visibility cone. Based on a particle model for the robots, various simple homing and collision-avoidance navigation strategies are derived first. Then, an analysis of the dynamical behavior of the interacting robots in unbounded spatial domains is made. The article concludes with the results of computer simulation studies of two or more interacting robots.     

An enhanced topological map for efficient and reliable mobile robot navigation with imprecise sensors

An enhanced topological mapping system for efficient and reliable navigation is presented. The map has a topological framework and some additional features. Firstly, it utilizes such rough metrical information as the length and orientation of the links. Secondly, it provides a reliable localization algorithm with which the robot first finds the interval describing the robot’s probable location by estimating the projected traveled distance using dead reckoning and then fine-tunes the estimation using landmark detection modules. Finally, it provides a planning algorithm with which the robot’s path is chosen so that the robot reaches the goal location as fast as possible without losing its way despite using such imprecise sensors as ultrasonic range finders.We have implemented and tested the proposed mapping system both on a simulator and a real mobile robot, the CAIR-2. This paper also describes landmark detection modules that utilize ultrasonic range finders. Although landmark detection modules are too simple and imprecise to estimate position by themselves, these experiments show that the proposed mapping system can reliably guide robots.     

Localization With Limited Sensing

Localization is a fundamental problem for many kinds of mobile robots. Sensor systems of varying ability have been proposed and successfully used to solve the problem. This paper probes the lower limits of this range by describing three extremely simple robot models and addresses the active localization problem for each. The robot, whose configuration is composed of its position and orientation, moves in a fully-known, simply connected polygonal environment. We pose the localization task as a planning problem in the robot's information space, which encapsulates the uncertainty in the robot's configuration. We consider robots equipped with: 1) angular and linear odometers; 2) a compass and contact sensor and; 3) an angular odometer and contact sensor. We present localization algorithms for models 1 and 2 and show that no algorithm exists for model 3. An implementation with simulation examples is presented.     

基于局部子地图方法的多机器人主动同时定位与地图创建

研究了多机器人在未知环境下以主动的方式协作完成同时定位与地图创建（SLAM）的问题．引入局部子地图方法,由每个机器人建立自身周围局部区域的子地图,使多个机器人之间的地图创建相互独立,从而对全局环境的SLAM问题进行分解．而每个机器人在建立局部子地图时将主动SLAM问题转化为多目标优化问题;机器人选取最优的控制输入,使定位与地图创建的准确性、信息增益以及多机器人之间的协调关系得到综合优化．最后,通过扩展的卡尔曼滤波器（EKF）对子地图进行融合得到全局地图．仿真结果验证了该方法的有效性．     

A generalized framework of dynamic role assignment for robot formation control

A dynamic role assignment algorithm is proposed in the paper for formation control of multiple mobile robots. The goal of the algorithm is to reassign a role for each robot automatically during a formation is forming or switching. Many formation control systems have been successfully implemented and validated by supporting experimental results. Nevertheless, this research aims at providing an efficient algorithm of role assignment for a class of formation control systems employing the concept of combinational optimization problems. Specifically, by exploring spatial relationship between robots and information of obstacles surrounding the robots, a character cost function is found to represent the degree of difficulty for a robot been assigned a specified role in a formation. Instead of using complex cost minimization procedure, a solution is provided by calculating the largest value of character set fitness, and a new formation is selected for robots accordingly. The developed algorithm is applied to the formation control of a group omni-directional driven robots. Simulation and experimentation are performed with real platform to verify the proposed algorithm and the results show that the performance of the proposed dynamic role assignment algorithm is efficient for robot formation control.     

基于全局特征点匹配的全局定位方法

针对传统全局定位方法存在对传感器要求多、计算量大的问题,提出了一种基于全局特征点匹配的移动机器人定位方法。该方法采用普通2D雷达作为传感器,在机器人建立全局地图的过程中同步地提取全局特征点,在全局定位算法中,通过建立局部地图和提取局部地图特征点,实时将局部地图特征点和全局地图特征点进行匹配后求解全局位姿。在两个数据集上的测试,结果优于蒙特卡罗自适应定位（adaptive Monte Carlo localization,AMCL）和Cartographer的全局定位效果,运算速度更快。结果表明,与已有的方法相比,该全局定位方法能够更快地完成全局定位和有效减少计算资源的消耗。     

多机器人任意队形分布式控制研究

本文针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队 形和队形向量不易确定的问题，通过由各智能体自主简单的确定自己的队形向量，从理论上 扩展基于队形向量的队形控制原理以生成任意队形，改进机器人的运动方式以提高收敛速度 ，提出一种快速收敛的机器人部队任意队形分布式控制算法．为了解决智能体机器人之间的 冲突问题，提出了一个通信协调模型．仿真实验和实际机器人实验均表明了算法的可行性和 有效性．     

Speech-based formation control of the multi-robot system

The article presents multiple pattern formation control of the multi-robot system using A* searching algorithm, and avoids the collision points moving on the motion platform. We use speech recognition algorithm to decide the various pattern formations, and program mobile robots to present the movement scenario on the grid-based motion platform. We have been developed some pattern formations to be applied in game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation and so on. The mobile robot contains a controller module, three IR sensor modules, a voice module, a wireless RF module, a compass module, and two DC servomotors. The controller of the mobile robot can acquire the detection signals from reflect IR sensor modules and the compass module, and decide the cross points of the aisle. The mobile robot receives the command from the supervised computer, and transmits the status of environment to the supervised computer via wireless RF interface. We develop the user interface of the multi-robot system to program motion paths for various pattern formation exchanges using the minimum displacement. Users can use speech to control the multiple mobile robots to execut pattern formation exchange. In the experimental results, users can speak the pattern formation. The speech recognition system receives the signal to decide the pattern formation. The multiple mobile robots can receive the pattern formation command from the supervised computer, and arrange the assigned pattern formation on the motion platform, and avoid other mobile robots.     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.