基于节点重要度的多机器人分布式巡逻策略

考虑到在多机器人巡逻任务中,待访问节点的重要程度存在差异是一种普遍现象,针对多数巡逻算法没有考虑节点重要程度的不同,导致所有节点的空闲时间趋于一致,从而造成重要节点访问频次不足、普通节点过度访问的问题,提出了一种基于节点重要度的分布式巡逻策略以优化节点访问频率、降低全局平均空闲时间。机器人计算周围节点空闲时间与重要度,在线决策目标节点,估计到达目标节点的时刻,并且将访问目标与估计到达时刻告知附近的同伴;为了避免某些节点被过度访问,在边缘节点的被访问频率低于最小访问频率时提高边缘节点的重要度,使得机器人可以尽快访问该点。最后,通过仿真分析了机器人数量、环境变化、重要度等因素对机器人完成持续巡逻任务的影响。结果表明,重要度大的节点被访问次数明显增加;在巡逻环境与机器人数量相同的前提下,该算法的异常值较少且平均空闲时间较小,多机器人持续巡逻性能表现较好。     

基于多步长的多机器人分布式巡逻算法研究

针对多机器人巡逻中多数算法只利用被访问节点的相邻节点信息，导致平均空闲时间增加的问题，提出了一种基于多步长的分布式巡逻算法。首先，利用无向图对环境进行建模，其中存在路径的2个节点互为邻居，引入重要度刻画节点所在区域的重要程度。其次，设计基于个体机器人的效用函数，其中利用了被访问节点邻居的邻居信息，函数在结合相邻节点空闲时间和重要度的同时，考虑了相邻节点邻居的局部平均空闲时间和节点个数，进而通过比较效用函数来指导个体机器人运动。实验结果表明，该算法在机器人数量逐渐增多时，系统的全局平均空闲时间也逐渐缩短且具有很好的稳定性，相较于其他几种对比算法，该算法更加适用于机器人数量较多时的巡逻任务。     

多机器人协同队形变换算法与实现

在面向任务的应用中,越来越多的任务需要多机器人协作才能完成.多机器人编队能有效提高任务完成效率并且保证可靠性,与单个机器人相比具有相当的优势.多个机器人要保持一个稳定的队形,其系统内部必须有信息的传递和交换.多机器人之间的队形保持难点在于每个机器人之间要实时获取其它机器人的位置坐标信息并且根据拓扑约束进行反馈调节.为解决上述问题,提出采用分布式控制和局部通信机制,避免使用全局绝对坐标.根据多机器人的物理约束、信息交换和控制策略建立并实现了多机器人编队的l-(φ)-l模型,与传统算法相比,实现了领航者根据跟随者的信息进行自身调节,提高了队形保持的稳定性.从仿真结果看,多个机器人能快速地进行队形变换,变换过程中没有相互的碰撞,并可以顺利通过障碍物,队形误差小.     

基于信息增益一致性的多机器人地图融合算法

针对多机器人协同SLAM(同步定位与地图构建)的地图融合中,由于通信距离受限或网络拓扑变化造成信息缺失、从而影响全局地图构建的问题,提出一种基于信息增益一致性原理的动态地图融合算法.该算法是完全分布式的,且不依赖于任何特殊的机器人通信网络结构.该算法利用机器人所测局部地图的历史数据和当前数据之间的新增信息,使每个机器人都能同步地获取一致的、最新的全局地图.在有限的网络连接条件下,所提出的地图融合算法能够通过渐近收敛的方式获得准确的全局地图.在每一次迭代中,每个机器人得到的全局地图都是无偏的.在实验中通过实际环境的RGB-D(彩色-深度)数据验证了算法的有效性.     

基于帕累托改进的多机器人动态任务分配算法

针对多机器人系统动态任务分配中存在的优化问题,在使用合同网初始任务分配的基础上提出了一种使用帕累托改进的任务二次分配算法。多机器人系统并行执行救火任务时,首先通过初始化任务分配将多机器人划分为若干子群;然后,每个子群承包某一救火任务,子群在执行任务的同时与就近子群进行帕累托改进确定需要迁移的机器人,实现两子群之间帕累托最优;最后,使用后序二叉树遍历对所有子群进行帕累托改进实现全局帕累托最优。理论分析和仿真结果表明,相较于强化学习算法和蚁群算法,所提算法的救火任务时间分别减少26.18%和37.04%;相较于传统合同网方法,所提算法在时间方面能够高效完成救火任务,在系统收益方面也具有明显优势。     

基于资源拍卖的农业多机器人任务分配

针对农田环境中多机器人协同作业的问题,提出一种基于资源的任务分配算法,用于在具有机器人资源的再填充站的长期任务中高效地执行多个任务.针对多机器人任务分配问题,对多机器人任务进行建模,并分析任务相关模型及任务能量指标.在进行拍卖算法任务分配时,在考虑机器人数目约束、工作时间约束、距离约束的基础上,加入任务执行能力的约束,考虑机器人在长期任务执行期间资源量消耗问题,使各个农机有序地为农田地块服务,降低整个系统的执行代价,提高任务完成量.利用MATLAB平台进行仿真实验,生成多机器人多任务点的分配优化结果,并设置多组不同数量的机器人,对比该算法同其他三种算法的效果.仿真结果表明,该算法可以有效地提高作业效率,在相同条件下使资源消耗量及任务完成量达到最优,证明了其优越性,同时计算结果与实际作业完成量更接近,提高了结果的精准性.     

基于时间条件算法的灭火机器人设计

详细介绍了灭火机器人的组装,在组装机器人的基础上,概括性的介绍了灭火机器人的各种算法及无火不进房算法中的时间算法和条件算法。为了让灭火机器人更加稳定的退出每个房间,结合时间算法和条件算法的优点,提出了时间条件算法,从而让机器人采用时间条件算法在迷宫中行走,并完成灭火任务。实验表明,此算法的应用使机器人在灭火过程中稳定性很高,并能及时避开迷宫墙壁;不但效果好,而且在很大程度上缩短了时间。     

多机器人地图融合方法研究

多机器人建图是实现机器人自主导航,完成复杂智能任务的关键.其中如何将不同机器人采集的数据融合到全局地图中,成了多机器人建图中的一个核心问题.文中采用独立探索、集中建图的探索策略,提出一种基于改进差异进化算法的多机器人概率栅格地图的融合.该算法在地图相似度的概念基础上,建立相异度函数,利用改进的进化算法搜索策略快速地搜索各局部地图之间的最大重叠部分,实现了多机器人系统栅格地图的融合,有效的解决了相对位置未知情况下的地图创建问题.通过实验验证了该方法正确、可行.     

基于劳动分工的群机器人地图创建探索策略研究

受社会性昆虫劳动分工的启发提出一种群机器人地图创建的探索策略,以提高群机器人创建地图的效率。当机器人所在顶点位置有未访问的路径时,机器人随机选择一条未访问路径进行访问;如果当前位置的所有路径都已被访问,机器人会根据响应函数对下一访问位置进行概率选择。对算法分别进行了不同地图规模和机器人数量的计算机仿真实验,根据算法评价指标(覆盖时间、路径重复覆盖次数和覆盖率)对实验结果进行了评价,并与随机选择的算法进行了对比,结果表明算法是可行、有效的。最后指出了下一步研究的方向。     

二进制粒子群算法在路径规划中的应用

全局路径规划是智能机器人的一个重要研究领域,将二进制粒子群算法应用于路径规划是一种新的尝试.提出一种机器人路径全局路径规划方法,介绍了利用改进的二进制粒子群算法进行路径规划的详细实现过程.机器人工作空间中的障碍物表示为多边形,对多边形顶点进行编号.利用二进制粒子群算法进行路径规划,粒子的长度定义为工作环境中障碍物顶点的个数,每一位为0或1表示路径是否经过该顶点.为了克服传统的二进制粒子群算法的早熟收敛问题,在改进的算法中采用了双重编码结构,并引入变异操作.最后给出仿真结果证明该方法的正确性和有效性.     

Gradual spatial pattern formation of homogeneous robot group

This paper proposes a gradual formation of a spatial pattern for a homogeneous robot group. The autonomous formation of spatial pattern is one of key technologies for the advancement of cooperative robotic systems because a pattern formation can be regarded as function differentiation of a multi-agent system. When multiple autonomous robots without a given local task cooperatively work for a global objective, the function differentiation is the first and indispensable step. For example, each member of cooperative insects or animals can autonomously recognize own local tasks through mutual communication with local members. There were a lot of papers that reported a spatial pattern formation of multiple robots, but the global information was supposed to be available in their approaches. It is however almost impractical assumption for a small robot to be equipped with an advanced sensing system for global localization due to robot’s scale and sensor size. The local information-based algorithm for the pattern formation is desired even if each robot is not equipped with a global localization sensor.We therefore propose a gradual pattern formation algorithm, i.e., a group of robots improves complexity of their pattern from to a simple pattern to a goal pattern like a polygon. In the algorithm, the Turing diffusion-driven instability theory is used so that it could differentiate roles of each robot in a group based only on local information. In experiment, we demonstrate that robots can make a few polygon patterns from a circle pattern by periodically differentiating robot’s roles into a vertex or a side. We show utilities of the proposed gradual pattern formation algorithm for multiple autonomous robots based on local information through some experiments.     

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.     

Distributed multi-robot coordination in area exploration

This paper proposes a reliable and efficient multi-robot coordination algorithm to accomplish an area exploration task given that the communication range of each robot is limited. This algorithm is based on a distributed bidding model to coordinate the movement of multiple robots. Two measures are developed to accommodate the limited-range communications. First, the distances between robots are considered in the bidding algorithm so that the robots tend to stay close to each other. Second, a map synchronization mechanism, based on a novel sequence number-based map representation and an effective robot map update tracking, is proposed to reduce the exchanged data volume when robot subnetworks merge. Simulation results show the effectiveness of the use of nearness measure, as well as the map synchronization mechanism. By handling the limited communication range we can make the coordination algorithms more realistic in multi-robot applications.     

VARIABLE PATROL PLANNING OF MULTI-ROBOT SYSTEMS BY A COOPERATIVE AUCTION SYSTEM

A cooperative auction system (CAS) is proposed to solve the large-scale multi-robot patrol planning problem. Each robot picks its own patrol points via the cooperative auction system and the system continuously re-auctions, based on the team work performance. The proposed method not only works in static environments but also considers variable path planning when the number of mobile robots increases or decreases during patrol. From the results of the simulation, the proposed approach demonstrates decreased time complexity, a lower routing path cost, improved balance of workload among robots, and the potential to scale to a large number of robots and is adaptive to environmental perturbations when the number of robots changes during patrol.     

Multi-Robot Graph Exploration and Map Building with Collision Avoidance: A Decentralized Approach

This paper proposes a decentralized multi-robot graph exploration approach in which each robot takes independent decision for efficient exploration avoiding inter-robot collision without direct communication between them. The information exchange between the robots is possible through the beacons available at visited vertices of the graph. The proposed decentralized technique guarantees completion of exploration of an unknown environment in finite number of edge traversals where graph structure of the environment is incrementally constructed. New condition for declaring completion of exploration is obtained. The paper also proposes a modification in incidence matrix so that it can be used as a data structure for information exchange. The modified incidence matrix after completion represents map of the environment. The proposed technique requires either lesser or equal number of edge traversals compared to the existing strategy for a tree exploration. A predefined constant speed change approach is proposed to address the inter-robot collision avoidance using local sensor on a robot. Simulation results verify the performance of the algorithm on various trees and graphs. Experiments with multiple robots show multi-robot exploration avoiding inter-robot collision.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Multi-robot area patrol under frequency constraints

Patrolling involves generating patrol paths for mobile robots such that every point on the paths is repeatedly covered. This paper focuses on patrolling in closed areas, where every point in the area is to be visited repeatedly by one or more robots. Previous work has often examined paths that allow for repeated coverage, but ignored the frequency in which points in the area are visited. In contrast, we first present formal frequency-based optimization criteria used for evaluation of patrol algorithms. Then, we present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path in minimal time, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly. We then present a set of algorithms for handling events along the patrol path. The algorithms differ in the way they handle the event, as a function of the time constraints for handling them. However, all the algorithms handle events while maintaining the patrol path, and minimizing the disturbance to the system.     

Distributed Control of Nonholonomic Robots Without Global Position Measurements Subject to Unknown Slippage Constraints

This paper studies the fully distributed formation control problem of multi-robot systems without global position measurements subject to unknown longitudinal slippage constraints.It is difficult for robots to obtain accurate and stable global position information in many cases,such as when indoors,tunnels and any other environments where GPS(global positioning system)is denied,thus it is meaningful to overcome the dependence on global position information.Additionally,unknown slippage,which is hard to avoid for wheeled robots due to the existence of ice,sand,or muddy roads,can not only affect the control performance of wheeled robot,but also limits the application scene of wheeled mobile robots.To solve both problems,a fully distributed finite time state observer which does not require any global position information is proposed,such that each follower robot can estimate the leader’s states within finite time.The distributed adaptive controllers are further designed for each follower robot such that the desired formation can be achieved while overcoming the effect of unknown slippage.Finally,the effectiveness of the proposed observer and control laws are verified by simulation results.     

A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.