目标围捕任务中搜索与预包围阶段的群机器人行为学习

为了进行群机器人协同作业,提出目标搜索中导航类集体行为学习策略.在使用具有闭环调节功能的动态任务分工方法进行任务分配、自组织地生成多个子群后,在子群中引入基于社会学习微粒群算法的机器人行为学习策略.在子群框架内,机器人各自独立地以感知的共同意向目标信号强度为标准对所有成员排序,将感知优于自己的机器人作为行为示范者.然后在搜索空间各维度上分别随机选择一个行为示范者,学习其在相应维度上的位置坐标,经构造得到搜索空间中自己的学习行为向量,由此决策自身的运动行为.仿真结果表明,在不需要学习全局社会经验的前提下,机器人能针对所属子群的共同意向目标进行协同作业,提高搜索效率.     

On a bio-inspired hybrid pheromone signalling for efficient map exploration of multiple mobile service robots

This paper presents a novel bio-inspired hybrid communication framework that incorporates the repelling behaviour of anti-aphrodisiac pheromones and attractive behaviour of pheromones for efficient map exploration of multiple mobile service robots. The proposed communication framework presents a scheme for robots to efficiently serve large areas of map, while cooperating with each other through proper pheromone deposition. This eliminates the need of explicitly programming each service robot to serve particular areas of the map. The paths taken by robots are represented as nodes across which pheromones are deposited. This reduces the search space for tracking pheromones and reduces data size to be communicated between robots. A novel pheromone deposition model is presented which takes into account the uncertainty in the robot’s position. This eliminates robots to deposit pheromones at wrong places when localization fails. The framework also integrates the pheromone signalling mechanism in landmark-based Extended Kalman Filter (EKF) localization and allows the robots to capture areas or sub-areas of the map, to improve the localization. A scheme to resolve conflicts through local communication is presented. We discuss, through experimental and simulation results, two cases of floor cleaning task, and surveillance task, performed by multiple robots. Results show that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming.     

Compound behaviors in pheromone robotics

We are pursuing techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. Using the biologically inspired notion of “virtual pheromone” messaging, we describe how many coordinated activities can be accomplished without centralized control. By virtue of this simple messaging scheme, a robot swarm can become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. We further describe a set of logical primitives for controlling the flow of virtual pheromone messages throughout the robot swarm. These enable the design of complex group behaviors mediated by messages exchanged between neighboring robots.     

基于改进免疫启发群聚集算法的群机器人自恢复方法

群机器人由许多简单的无差别的机器人组成,是多机器人系统的一个重要研究方向。虽然其相比个体机器人有良好的容错性和鲁棒性,但是在机器人发生局部故障--有信息交互能力但无驱动能力时,群机器人系统会受到影响。针 对这一问题,以基于生物免疫系统原理的肉芽肿形成算法为基础,引入离散粒子群算法选取最优的自恢复策略,使群机器人系统实现故障自恢复并更快更有效地完成任务。仿真实验结果表明该算法在群机器人自恢复系统中具有良好的效果。     

Using Virtual Pheromones and Cameras for Dispersing a Team of Multiple Miniature Robots

To safely and efficiently guide personnel of search and rescue operations in disaster areas, swift gathering of relevant information such as the locations of victims, must occur. Using the concept of ‘repellent virtual pheromones’ inspired by insect colony coordination behaviors, miniature robots can be quickly dispersed to survey a disaster site. Assisted by visual servoing, dispersion of the miniature robots can quickly cover an area. An external observer such as another robot or an overhead camera is brought into the control loop to provide each miniature robot estimations of the positions of all of the other near-by robots in the robotic team. These miniature robots can then move away from the other near-by robots on the team, resulting in the robot collective becoming swiftly distributed through the local area. The technique has been simulated with differing pheromone persistence levels and implemented using the miniature Scout robots, developed by the Center for Distributed Robotics at the University of Minnesota, which are well-suited to surveillance and reconnaissance missions.     

Get in touch: cooperative decision making based on robot-to-robot collisions

We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.     

Towards temporal verification of swarm robotic systems

A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.     

A PSO-based algorithm designed for a swarm of mobile robots

This paper presents an algorithm called augmented Lagrangian particle swarm optimization with velocity limits (VL-ALPSO). It uses a particle swarm optimization (PSO) based algorithm to optimize the motion planning for swarm mobile robots. Considering problems with engineering constraints and obstacles in the environment, the algorithm combines the method of augmented Lagrangian multipliers and strategies of velocity limits and virtual detectors so as to ensure enforcement of constraints, obstacle avoidance and mutual avoidance. All the strategies together with basic PSO are corresponding to real situations of swarm mobile robots in coordinated movements. This work also builds a swarm motion model based on Euler forward time integration that involves some mechanical properties such as masses, inertias or external forces to the swarm robotic system. Simulations show that the robots moving in the environment display the desired behavior. Each robot has the ability to do target searching, obstacle avoidance, random wonder, acceleration or deceleration and escape entrapment. So, in summary due to the characteristic features of the VL-ALPSO algorithm, after some engineering adaptation, it can work well for the planning of coordinated movements of swarm robotic systems.     

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.     

基于确定型有穷自动机的机器人行为控制单元

基于机器人基本行为的控制方法是机器人能够执行许多高级算法的基本条件,且能有效的减少重编程时的代码量,适用于大规模机器人群体中的无线程序烧录,减少通信量。针对课题研发的需求,提出一种基于确定型有穷自动机的机器人行为控制的数学模型,并使用Picoblaze处理器在FPGA上实现了该数学模型,实验表明本文的方法能大幅度的减少代码更新时的通信量。     

绝对定位机制下目标位置估计辅助的群机器人搜索

基于扩展微粒群算法模型控制群机器人协同搜索目标时,成员机器人在社会经验和自身认知,主要是社会经验引导下逐步向目标趋近.由于社会经验仅从成员机器人的认知中“选举”产生,未形式化地融合多个机器人的经验,因此文中从群机器人通信子系统在本质上属于无线传感器网络的事实出发,引入集体决策机制,改进社会经验的生成模式.用无线传感器网络中的测距定位方法来估计目标位置,并将估计值作为社会经验引入现有模型.仿真结果表明,当群体规模够大时,采用文中社会经验生成模式可使协同搜索速度得到提高.     

Aibo and Webots: Simulation,wireless remote control and controller transfer

This article introduces a new software tool that provides an accurate simulation of Sony Aibo robots and the capability to transfer controller programs from the simulation to the real robot. Five components are described: (1) a simulated physics-based model of the Sony Aibo ERS-210(A) and ERS-7 quadruped robots; (2) a graphical user interface for controlling the simulated and real robots; (3) a wireless communication protocol for controlling the robot from within Webots; (4) software components on the robot that enable remote control; and (5) a method for cross-compiling Webots robot controllers. The complete system has been calibrated and proof tested. It enables simultaneous control of both a simulated and a real Aibo robot and provides the user with a platform for convenient robot programming without any knowledge of the underlying robot firmware.     

Multiple targets enclosure by robotic swarm

Target enclosure by autonomous robots is useful for many practical applications, for example, surveillance of disaster sites. Scalability is important for autonomous robots because a larger group is more robust against breakdown, accidents, and failure. However, since the traditional models have discussed only the cases in which minimum number of robots enclose a single target, there has been no study on the utilization of the redundant number of robots. In this paper, to achieve a highly scalable target enclosure model about the number of target to enclose, we introduce swarm based task assignment capability to Takayama’s enclosure model. The original model discussed only single target environment but it is well suited for applying to the environments with multiple targets. We show the robots can enclose the targets without predefined position assignment by analytic discussion based on switched systems and a series of computer simulations. As a consequence of this property, the proposed robots can change their target according to the criterion about robot density while they enclose multiple targets.     

A Constraint-Based Robotic Soccer Team

It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.     

基于微粒群优化的有限通信多机器人气味寻源

考虑机器人间的通信受限约束,将机器人抽象为微粒,提出基于微粒群优化的多机器人气味寻源方法.首先,采用结合斥力函数的策略,引导机器人快速搜索烟羽;然后,基于无线信号对数距离损耗模型,估计机器人间的通讯范围,据此形成微粒群的动态拓扑结构,并确定微粒的全局极值;最后,将传感器的采样/恢复时间融入微粒更新公式,以跟踪烟羽.将所提出方法应用于3个不同场景的气味寻源,实验结果验证了该方法的有效性.     

Simulation and control of distributed robot search teams

This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is to, once deployed in an environment (floor-plan) with multiple rooms, cover as many rooms as possible. The simulated robots are capable of navigation through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits.     

A virtual centrifugal force based navigation algorithm for explorative robotic tasks in unknown environments

In many robotic tasks, there is no a priori knowledge of the environment. This makes it necessary for robots to explore the environment. Navigation algorithms for robots to map the environment completely in a short time play a very important role in the robotic task completion. A navigation algorithm based on virtual centrifugal force is proposed to complete the robotic exploration of the unknown environment using rang sensors in this paper. Collisions between a robot and an obstacle or between robots can be avoided with the application of the proposed navigation rules. The kinematics and dynamics equations of robots adopting the algorithm are also given. The simulation experiments demonstrate the operation of the algorithm. Several simulation experiments of various representative robotic tasks are carried out, based on the explorative navigation algorithm, which successfully validate the virtual centrifugal force based navigation algorithm.     

Evolving autonomous specialization in congested path formation task of robotic swarms

Redundancy in the number of robots is a fundamental feature of robotic swarms to confer robustness, flexibility, and scalability. However, robots tend to interfere with each other in a case, where multiple robots gather in a spatially limited environment. The aim of this paper is to understand how a robotic swarm develops an effective strategy to manage congestion. The controllers of the robots are obtained by an evolutionary robotics approach. The strategy of managing congestion is observed in the process of generating a collective path of robots visiting two landmarks alternately. The robotic swarm exhibits autonomous specialization that the robots traveling inside the path activate the LEDs, while the robots in the outer side deactivate them. We found that the congestion is regulated in an emergent way of autonomous specialization by the result of an artificial evolution.     

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.