Task Modelling in Collective Robotics

Does coherent collective behaviour require an explicit mechanism of cooperation? In this paper, we demonstrate that a certain class of cooperative tasks, namely coordinated box manipulation, are possible without explicit communication or cooperation mechanisms. The approach relies on subtask decomposition and sensor preprocessing. A framework is proposed for modelling multi-robot tasks which are described as a series of steps with each step possibly consisting of substeps. Finite state automata theory is used to model steps with state transitions specified as binary sensing predicates called perceptual cues. A perceptual cue (Q), whose computation is disjoint from the operation of the automata, is processed by a 3-level finite state machine called a Q-machine. The model is based on entomological evidence that suggests local stimulus cues are used to regulate a linear series of building acts in nest construction. The approach is designed for a redundant set of homogeneous mobile robots, and described is an extension of a previous system of 5 box-pushing robots to 11 identical transport robots. Results are presented for a system of physical robots capable of moving a heavy object collectively to an arbitrarily specified goal position. The contribution is a simple task-programming paradigm for mobile multi-robot systems. It is argued that Q-machines and their perceptual cues offer a new approach to environment-specific task modelling in collective robotics.     

REDSIEX: A Cooperative Network of Expert Systems with Blackboard Architectures

The blackboard architecture, originally developed for the system that permits the comprehension of language, HEARSAY II, has later been used in a great variety of domains and in various environments for the construction of systems. From the classic architecture of HEARSAY II, many applications, generalizations, extensions and refinements have been developed. In this paper we present REDSIEX, (RED de SIstemas EXpertos) which is a network of expert systems within a blackboard architecture, for the cooperation solution of distributed problems. The REDSIEX system inherits various of the elements defined by the architecture of HEARSAY II and incorporates new components and organization. These produce a very characteristic and exclusive global work style in the solution of problems, within a conceptual framework of emergent control. The main structural and functional characteristics of REDSIEX are discussed.     

Cooperative autonomous search,grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

This paper addresses the problem of autonomous cooperative localization, grasping and delivering of colored ferrous objects by a team of unmanned aerial vehicles (UAVs). In the proposed scenario, a team of UAVs is required to maximize the reward by collecting colored objects and delivering them to a predefined location. This task consists of several subtasks such as cooperative coverage path planning, object detection and state estimation, UAV self‐localization, precise motion control, trajectory tracking, aerial grasping and dropping, and decentralized team coordination. The failure recovery and synchronization job manager is used to integrate all the presented subtasks together and also to decrease the vulnerability to individual subtask failures in real‐world conditions. The whole system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where it achieved the highest score and won Challenge No. 3—Treasure Hunt. This paper does not only contain results from the MBZIRC 2017 competition but it also evaluates the system performance in simulations and field tests that were conducted throughout the year‐long development and preparations for the competition.     

集群机器人研究综述

首先,给出了集群机器人的定义与基本特征,概括了相对于传统多机器人控制方法的优势.然后,总结了集群机器人的主要设计与分析方法.将集群机器人研究归纳为空间组织、集群导航、集群决策以及其他集群行为等4类,综述了各类别近十年的主要研究成果.最后,分析总结了当前集群机器人研究面临的挑战与关键科学问题,并对未来发展方向进行了展望.     

从集群智能角度分析协同驾驶:综述和展望

协同驾驶是集群智能技术的典型应用之一.和集群机器人相比,协同驾驶既有集群智能技术的共性问题,也有特性问题.对协同驾驶与集群机器人对比研究,有助于更好理解协同驾驶问题的内在难点,也有助于推动集群智能技术的发展,完善复杂系统科学.本文从集群智能研究的角度出发,对协同驾驶进行反思综述.本文首先介绍协同驾驶的研究背景和意义,然后从集群智能的角度进一步分析协同驾驶和集群机器人的区别,接着介绍协同驾驶的关键技术,并着重强调协同规划在协同驾驶中的特殊性与重要性,最后分析了世界各国协同驾驶发展现状,并针对现阶段我国协同驾驶发展的难点提出一些建议,同时对协同驾驶的未来进行展望.     

Al‐Robotics team: A cooperative multi‐unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al‐Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al‐Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.     

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.     

Remote Interaction with Mobile Robots

This paper describes an architecture, which can be used to build remote laboratories to interact remotely via Internet with mobile robots using different interaction devices. A supervisory control strategy has been used to develop the remote laboratory in order to alleviate high communication data rates and system sensitivity to network delays. The users interact with the remote system at a more abstract level using high level commands. The local robot's autonomy has been increased by encapsulating all the robot's behaviors in different types of skills. User interfaces have been designed using visual proxy pattern to facilitate any future extension or code reuse. The developed remote laboratory has been integrated into an educational environment in the field of indoor mobile robotics. This environment is currently being used as a part of an international project to develop a distributed laboratory for autonomous and teleoperated systems (IECAT, 2003).     

Application of a Possibilitic-Based Approach to Mobile Robotics

Possibility theory offers a nice setting for information combination or data fusion. This attractiveness arises from the elastic constraints that govern the basic concepts pertaining to this theory. Consequently, many combination modes are available ranging from the conjunctive to the disjunctive passing through the compromise mode. Therefore the problem of what is the suitable combination mode for a given situation is still open. The adaptive rule proposed by Dubois and Prade contributes partly to this problem, and has been successfully employed in several applications like robotics. In this paper we apply the recently new combination rule referred to as progressive rule, which permits us to handle robustness with respect to shape modelling and takes account for a possible presence of erroneous information, to mobile robotics context. The rule explicitly accounts for the distance between each alternative and the consensus zone. The rule is then incorporated into a general scheme of fusion methodology, which allows a transformation of raw inputs into meaningful and homogeneous information that will be refined by the progressive rule. A robotics application corresponding to mobile robot localization in a structured environment is carried out. The feasibility of the possibilistic approach is demonstrated by a comparison with a standard method based on Kalman filter.     

Robot Awareness in Cooperative Mobile Robot Learning

Most of the straight-forward learning approaches in cooperative robotics imply for each learning robot a state space growth exponential in the number of team members. To remedy the exponentially large state space, we propose to investigate a less demanding cooperation mechanism—i.e., various levels of awareness—instead of communication. We define awareness as the perception of other robots locations and actions. We recognize four different levels (or degrees) of awareness which imply different amounts of additional information and therefore have different impacts on the search space size ((0), (1), (N), o(N),¹ where N is the number of robots in the team). There are trivial arguments in favor of avoiding binding the increase of the search space size to the number of team members. We advocate that, by studying the maximum number of neighbor robots in the application context, it is possible to tune the parameters associated with a (1) increase of the search space size and allow good learning performance. We use the cooperative multi-robot observation of multiple moving targets (CMOMMT) application to illustrate our method. We verify that awareness allows cooperation, that cooperation shows better performance than a purely collective behavior and that learned cooperation shows better results than learned collective behavior.