Development of the remote-controlled platform truck for handling heavy object and the remote control human interface for the disaster response

General interest for robotic technology has been increased by the public and the media after Fukushima Daiichi Nuclear Power Plant (hereafter referred to as 1F) disaster. Especially, robots which can work under the very severe condition where personnel cannot access have demands for development. As to respond to such high demands, NEDO established ‘Disaster response unmanned systems development project’ in 2012.[1] This project is specialized in the development of various remote-controlled equipment, such as remote-controlled platform truck and remote control human interface under ‘Mobile Robot Development’. Remote-controlled platform truck is designed to safely and surely deliver robots and supplies, instead of using stairs and elevator, etc. in and out of building where it is too critical for personnel to work under. Remote control human interface for robots is designed to standardize the command and operation screen for operator, based on opinions from project members, and manned facility construction.[2] In this article, mechanical structure and development tasks for remote-controlled platform truck, and commonalization approach for operation and camera display of various remote-controlled equipment and robot for remote control human interface are stated. Background: due to hydrogen explosion triggered by The Great East Japan Earthquake on 11 March 2011, a reactor building at Fukushima Daiichi Nuclear Power Plant was severely damaged. It is required to reduce high radiation dose in the atmosphere of the reactor building to perform restoration. To pursue decontamination of the reactor building, equipment to lift and carry decontamination devices to upper floor are required.     

Cooperative task excecution of a search and rescue mission by a multi-robot team

Rescue robots have proved to be an extremely useful work partner for urban search and rescue (USAR) missions. Human rescuers who carry out these missions frequently enter dangerous zones to search for survivors; however, due to the unstable nature of collapsed buildings or objects, their lives may also be threatened. For this reason, in order to reduce life-threatening risks, rescue robots are deployed to carry out the job instead. Rescuers can now operate the robots at a safe place while the missions are carried out. When the robots have gathered enough information about the location of the victims and data about their physical conditions, rescuers can then enter the disaster site with enough knowledge to avoid harm and rescue the victims in the shortest time possible. In this paper, we introduce examples of 'effective multiple robot cooperative activities' and 'a study of the number of robots and operators in a multi-robot team' from our experiences gained from participating in RoboCup Rescue competitions.     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

A distributed architecture for supervision of autonomous multi-robot missions

Realizing long-term autonomous missions involving teams of heterogeneous robots is a challenge. It requires mechanisms to make robots react to disturbances or failures that will arise during the mission, while trying to successfully achieve the mission in cooperation. This paper presents HiDDeN, a distributed deliberative architecture that manages the execution of a hierarchical plan. This plan has initially been computed offline, ensuring some military operational constraints of the mission. Each robot’s supervisor then executes its own part of the plan, and reacts to failures using a hierarchical repair approach. This hierarchical repair has been designed with the sake of ensuring operational constraints, while reducing the need of communication between robots, as communication may be intermittent or even nonexistent when the robots operate in completely separate environments. HiDDeN’s robustness and scalability is evaluated with simulations. Experiments with an autonomous helicopter and an autonomous underwater vehicle have been realized and are presented as the defining point of our contribution.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

Airborne radiation mapping: overview and application of current and future aerial systems

Nuclear power and associated activities are never far from scrutiny, the apparent advantages of the technology are juxtaposed by the risk of incidents perceived as being catastrophic. If a major nuclear incident was to occur, an important aspect of the response management to any radionuclide release would be the need to rapidly establish the spatial distributions and quantities of these released radionuclides, their type in addition to their corresponding activity. The data received from surveys would directly inform evacuation plans, on-site incident management strategies as well as protecting both workforce and public from harm. The disaster at the Fukushima Daiichi Nuclear Power Plant in 2011 is perhaps the best example of the requirement for real time data collection to inform crucial decisions. Previous reviews of the event have observed that because the static on-site radiation detector network was destroyed by the 15 m high tsunami (following the magnitude 9.0 Great Tōhoku earthquake), it was not possible to immediately determine the radionuclide activity in the area and the danger presented to the responding workforce. Such preceding works have retrospectively highlighted the usefulness of unmanned aerial systems in providing real-time data within nuclear and non-nuclear settings. The establishment of an arbitrary 20 km exclusion zone surrounding the Fukushima Daiichi plant, with the displacement of over 150,000 people, has been viewed by many as an over-reaction – with many not having been required to be evacuated. This review examines and evaluates the previous as well as current work on aerial radiation monitoring and the future improvement that might be delivered by a combined three-dimensional (3D) radiation mapping platform. Combining detailed 3D topographical mapping with radiation surveying has powerful implications for the way that radiological contamination across a site might be measured and displayed in the future, both following radiological release events and in routine site monitoring.     

An investigation of climbing up stairs for an inchworm robot

In this research, an inchworm-type robot is being developed for the purpose of rescue missions. This robot has the ability to traverse obstacles such as stairs with fewer joints than legged robots. A self-standing inchworm robot, which has five links and four joints, is produced for trial purposes. In order for this robot to be able to climb up stairs, the kinematical analysis and the development of the program were investigated. As a result, the effectiveness of this robot is confirmed experimentally.     

Hybrid mission planning with coalition formation

The increase in robotic capabilities and the number of such systems being used has resulted in opportunities for robots to work alongside humans in an increasing number of domains. The current robot control paradigm of one or multiple humans controlling a single robot is not scalable to domains that require large numbers of robots and is infeasible in communications constrained environments. Robots must autonomously plan how to accomplish missions composed of many tasks in complex and dynamic domains; however, mission planning with a large number of robots for such complex missions and domains is intractable. Coalition formation can manage planning problem complexity by allocating the best possible team of robots for each task. A limitation is that simply allocating the best possible team does not guarantee an executable plan can be formulated. However, coupling coalition formation with planning creates novel, domain-independent tools resulting in the best possible teams executing the best possible plans for robots acting in complex domains.     

Real-time exploration of a multi-robot rescue system in disaster areas

A system procedure is proposed for a multi-robot rescue system that performs real-time exploration over disaster areas. Real-time exploration means that every robot exploring the area always has a communication path to human operators standing by at a base station and that the communication path is configured by ad hoc wireless networking. Real-time exploration is essential in multi-robot systems for USAR (urban search and rescue) because operators must communicate with every robot to support the victim detection process and ad hoc networking is suitable to configure a communication path among obstacles. The proposed system procedure consists of the autonomous classification of robots into search and relay types and behavior algorithms for each class of robot. Search robots explore the areas and relay robots act as relay terminals between search robots and the base station. The rule of the classification and the behavior algorithm refer to the forwarding table of each robot constructed for ad hoc networking. The table construction is based on DSDV (destination-sequenced distance vector) routing that informs each robot of its topological position in the network and other essentials. Computer simulations are executed with a specific exploration strategy of search robots. The results show that a multi-robot rescue system can perform real-time exploration with the proposed system procedure and reduce exploration time in comparison with the case where the proposed scheme is not adopted.     

Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach

In this paper, a novel heuristic algorithm is proposed to solve continuous non-linear optimization problems. The presented algorithm is a collective global search inspired by the swarm artificial intelligent of coordinated robots. Cooperative recognition and sensing by a swarm of mobile robots have been fundamental inspirations for development of Swarm Robotics Search & Rescue (SRSR). Swarm robotics is an approach with the aim of coordinating multi-robot systems which consist of numbers of mostly uniform simple physical robots. The ultimate aim is to emerge an eligible cooperative behavior either from interactions of autonomous robots with the environment or their mutual interactions between each other. In this algorithm, robots which represent initial solutions in SRSR terminology have a sense of environment to detect victim in a search & rescue mission at a disaster site. In fact, victim’s location refers to global best solution in SRSR algorithm. The individual with the highest rank in the swarm is called master and remaining robots will play role of slaves. However, this leadership and master position can be transitioned from one robot to another one during mission. Having the supervision of master robot accompanied with abilities of slave robots for sensing the environment, this collaborative search assists the swarm to rapidly find the location of victim and subsequently a successful mission. In order to validate effectiveness and optimality of proposed algorithm, it has been applied on several standard benchmark functions and a practical electric power system problem in several real size cases. Finally, simulation results have been compared with those of some well-known algorithms. Comparison of results demonstrates superiority of presented algorithm in terms of quality solutions and convergence speed.     

Semi-autonomous serially connected multi-crawler robot for search and rescue

In this paper, we develop a semi-autonomous serially connected multi-crawler robot for search and rescue. In large-scale disasters, such as earthquakes and tornadoes, the application of rescue robots to search for survivors under rubble would be beneficial. Snake-like robots (robots composed of serially connected units) are an effective candidate for such robots. Their long body enables them to overcome obstacles, and they can move into narrow spaces because of their thin shape. However, conventional snake-like robots have significant problems with operability. The numerous degrees of freedom of their bodies require complex operation to overcome obstacles, and training is required for the operators. Thus, survivors or community members cannot operate conventional robots to search for victims, despite the availability of such rescue robots. Here, we address this problem and develop a semi-autonomous serially connected multi-crawler robot designed for non-trained operators, such as community members or rescued survivors. It can be controlled easily by a conventional two-channel user interface with levers for turning and straight line motion. To demonstrate the effectiveness of our proposed mechanism, a prototype robot was developed and experiments were conducted. The results confirm that the proposed robot had both higher operability and higher mobility than conventional robots.     

Planetary exploration by a mobile robot: Mission teleprogramming and autonomous navigation

Sending mobile robots to accomplish planet exploration missions is scientifically promising and technologically challenging. We present in this paper a complete approach that encompasses the major aspects involved in the design of a robotic system for planetary exploration. It includes mission teleprogramming and supervision at a ground station, and autonomous mission execution by the remote mobile robot. We have partially implemented and validated these concepts. Experimental results illustrate the approach and the results.     

移动机器人的时间最优编队

针对移动机器人的最速编队问题,结合路径规划和任务分解,提出一种分派问题的新解法和时间最优的编队策略。该策略充分考虑了障碍物环境约束和各机器人运动时的相互影响,通过将系统整体路径规划的复杂问题分解为独立路径规划问题和冲突协调问题来分别求解,降低了计算的复杂性,并能了快编队。     

Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

This paper addresses the interest of using punctual versus continuous coordination for mobile multi-robot systems where robots use auction sales to allocate tasks between them and to compute their policies in a distributed way. In continuous coordination, one task at a time is assigned and performed per robot. In punctual coordination, all the tasks are distributed in Rendezvous phases during the mission execution. However, tasks allocation problem grows exponentially with the number of tasks. The proposed approach consists in two aspects: (1) a control architecture based on topological representation of the environment which reduces the planning complexity and (2) a protocol based on sequential simultaneous auctions (SSA) to coordinate Robots’ policies. The policies are individually computed using Markov Decision Processes oriented by several goal-task positions to reach. Experimental results on both real robots and simulation describe an evaluation of the proposed robot architecture coupled wih the SSA protocol. The efficiency of missions’ execution is empirically evaluated regarding continuous planning.     

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.     

自主柔性变形蛇形机器人控制系统设计

针对搜救机器人对多信息获取与处理、远程监控与运动控制的实时高性能需求,设计了以ARM微处理器STM32为核心、多传感器融合的自主柔性变形蛇形机器人控制系统,实现了机器人的远程监控与运动控制、多传感器环境信息采集等功能。整个控制系统具有良好的扩展性、硬件可裁剪性。通过模拟灾难废墟场景实验,结果表明：蛇形机器人控制系统可实现多信息的实时准确无线通信,在不同的环境中,具有良好的多步态运动稳定性和自主移动性能。     

Cooperative exploration based on supervisory control of multi-robot systems

When multiple mobile robots cooperatively explore an unknown environment, the advantages of robustness and redundancy are guaranteed. However, available traditional economy approaches for coordination of multi-robot systems (MRS) exploration lack efficient target selection strategy under a few of situations and rely on a perfect communication. In order to overcome the shortages and endow each robot autonomy, a novel coordinated algorithm based on supervisory control of discrete event systems and a variation of the market approach is proposed in this paper. Two kinds of utility and the corresponding calculation schemes which take into account of cooperation between robots and covering the environment in a minimal time, are defined. Different moving target of each robot is determined by maximizing the corresponding utility at the lower level of the proposed hierarchical coordinated architecture. Selection of a moving target assignment strategy, dealing with communication failure, and collision avoidance are modeled as behaviors of each robot at the upper level. The proposed approach distinctly speeds up exploration process and reduces the communication requirement. The validity of our algorithm is verified by computer simulations.     

多层建筑应急疏散仿真

以多层建筑为背景,研究应急疏散问题,基于改进的路径规划算法,并将多智能体技术应用在模型间的通信交流上.采用机器人感知周围环境,通过设置机器人个数以及初始位置,对灾情中室内被困人员进行智能搜救,并采集现场实时数据,作出决策分析.机器人实时感知现场状况的变化,引导人员疏散,并将实时数据传输给施救人员,采取进一步救援措施.结果表明,该三维仿真技术为有效减少人员疏散中的伤亡和最佳救援方案的制定提供了参考,具有一定的现实指导意义.     

Lifelong Adaptation in Heterogeneous Multi-Robot Teams: Response to Continual Variation in Individual Robot Performance

Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment.     

Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision

This paper deals with the problem of deploying a team of flying robots to perform surveillance-coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion of the mission is the knowledge of the terrain’s morphology. The focus of this paper is on the implementation of a two-step procedure that allows us to optimally align a team of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area using a novel monocular-vision-based approach. A?state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, autonomously, building an incremental map of the environment. The map generated is processed and serves as an input to an optimization procedure using the cognitive, adaptive methodology initially introduced in Renzaglia et?al. (Proceedings of the IEEE international conference on robotics and intelligent system (IROS), Taipei, Taiwan, pp.?3314–3320, 2010). The output of this procedure is the optimal arrangement of the robots team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.