A flooding algorithm for multirobot exploration

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS.     

Simultaneous exploration and segmentation for search and rescue

We consider the problem of active victim segmentation during a search‐and‐rescue (SAR) exploration mission. The robot is equipped with a multimodal sensor suite consisting of a camera, lidar, and pan‐tilt thermal sensor. The robot enters an unknown scene, builds a 3D model incrementally, and the proposed method simultaneously (a) segments the victims from incomplete multimodal measurements and (b) controls the motion of the thermal camera. Both of these tasks are difficult due to the lack of natural training data and the limited number of real‐world trials. In particular, we overcome the absence of training data for the segmentation task by employing a manually designed generative model, which provides a semisynthetic training data set. The limited number of real‐world trials is tackled by self‐supervised initialization and optimization‐based guiding of the motion control learning. In addition to that, we provide a quantitative evaluation of the proposed method on several real testing scenarios using the real SAR robot. Finally, we also provide a data set which will allow for further development of algorithms on the real data.     

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.     

Multirobot exploration for search and rescue missions: A report on map building in RoboCupRescue 2009

In the future, mobile robots may be able to assist rescue crews in search and rescue missions that take place in the dangerous environments that result from natural or man‐made disasters. In 2006, we launched a research project to develop mobile robots that can rapidly collect information in the initial stages of a disaster. One of our important objectives is three‐dimensional (3D) mapping, which can be a very useful tool for assisting rescue crews in strategizing rescue missions. To realize this 3D mapping, we identified five issues that we needed to address: (1) autonomous traversal of uneven terrain, (2) development of a system for the continuous acquisition of 3D data of the environment, (3) coverage path planning, (4) centralization of map data obtained by multiple robots, and (5) fusion of map data obtained by multiple robots. We solved each problem through our joint research. Each research institute in our group took charge of solving one of the above issues according to its area of expertise. We integrated these solutions to perform 3D mapping using our tracked vehicle, Kenaf. To validate our integrated autonomous 3D mapping system, we participated in RoboCupRescue 2009 and demonstrated our system using multiple robots on the RoboCupRescue field. In this paper, we introduce our mapping system and report the mapping results obtained at the RoboCupRescue event. © 2011 Wiley Periodicals, Inc.     

A generic multirobot control experimental system

This article describes a generic experimental system that implemented the activity controller theory. The controller has the ability to coordinate any number of robots (and other automated machines) and to respond to the random arrival of components, so as to efficiently assemble various mixes of products. The work also briefly presents the structure of the coordination system, and a solution frame to the dynamic collision avoidance problem. The computer-controlled experimental system is used to simultaneously operate two robots sharing tasks and auxiliary devices in an assembly cell.     

A spatial orthogonal allocation and heterogeneous cultural hybrid algorithm for multirobot exploration mission planning

A spatial orthogonal allocation method is devised for multirobot tasks allocation.A 3D space model is adopted to describe exploration mission;meanwhile spatial orthogonal tentative technology is utilized to update the attractor position for load balance.Heterogeneous interactive cultural hybrid architecture is proposed to solve a robot route planning problem;it utilizes good-point-set to initialize population spaces,redefine novel evolution model and particle evolution ability,and introduce near-neighbor lo...     

Distributed multirobot exploration, mapping, and task allocation

We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams—of what we refer to as Centibots—consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.     

A lightweight autonomous MAV for indoor search and rescue

Micro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser‐based SLAM module and a custom‐designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor‐outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree‐based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award‐winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM .     

Human-robot teaming for search and rescue

This work establishes an architecture for Urban Search and Rescue and a methodology for mixing real-world and simulation-based testing. A sensor suite and sensor fusion algorithm for robust victim detection permits aggregation of sensor readings from various sensors on multiple robots. We have embarked on a research program focusing on the enabling technologies of effective USAR robotic rescue devices. The program is also researching system-level design, evaluation, and refinement of USAR rescue architectures that include teams of sensor-laden robots and human rescuers. In this paper, we present highlights from our research, which include our multiagent system (MAS) infrastructure, our simulation environment, and our approach to sensor fusion and interface design for effective robotic control.     

A novel 3D sensory system for robot-assisted mapping of cluttered urban search and rescue environments

In this paper, the first application of utilizing a unique 3D sensor for sequential 3D map building in unknown cluttered urban search and rescue (USAR) environments is proposed. The sensor utilizes a digital fringe projection and phase shifting technique to provide real-time 2D and 3D sensory information of the environment. The proposed sensor is unique over current technologies in that high-resolution 3D information of rubble filled environments can be acquired from the single sensor at a speed of 30 frames per second (fps). Furthermore, we propose the development of a novel robust and reliable landmark identification technique that utilizes both 2D and 3D depth images taken by the sensor for 3D mapping. Preliminary experiments show the potential of the real-time 3D sensory system and landmark identification scheme for robotic 3D mapping in unknown cluttered USAR-like environments.     

Morphology control in a multirobot system

In this article, we propose a distributed control mechanism for a self-propelled, self-assembling robotic system that allows robots to form specific, connected morphologies. Global morphologies are grown using only local visual perception. Robots that are part of the connected entity indicate where new robots should attach to grow the local structure appropriately. We demonstrate the efficacy of the mechanism by letting groups of seven real robots self-assemble into four different morphologies: line, star, arrow, and rectangle.     

多机器人系统中的机器人运动控制

针对多机器人系统中机器人运动控制的要求和特点,提出了基于Server+IPC+PLC架构的移动机器人运动控制系统方案,解决了系统互连中存在的一些问题。建立移动机器人的运动学模型,设计基于分解运动速度控制的机器人运动轨迹跟踪算法,并通过仿真研究验证算法的有效性。     

A joint network for disaster recovery and search and rescue operations

Disasters are exceptional events that are either man made, such as terrorist attacks, or natural, such as earthquakes, wildfires and floods. Disasters create emergency situations and cause physical and social disorder. In these emergency situations, food, water, shelter, protection and medical help are needed, and the effort needed to provide these basic services to the victims must be coordinated quickly via a reliable communication network. A disaster recovery network is used to provide emergency support to both the disaster victims and the crewmembers that are helping the victims, and to provide a communication infrastructure in the disaster affected area. The disaster relief operation also involves searching for and locating the survivors, and then rescuing them. Currently this process involves manual search in the disaster area, which is also time consuming. A novel network architecture called the Portable Disaster Recovery Network is presented in this paper that enables survivors in a disaster area or a non-disaster related search-and-rescue situation to report their locations to a Command Center. This enables first responders to quickly rescue the survivors from these areas. This paper analyzes the performance of random walk models of the movement of survivors in the Portable Disaster Recovery Network.     

An image recognition system aimed at search activities using cyber search and rescue dogs

Disaster response presents major challenges for robotics and computer vision alike. The Cyber‐Enhanced Canine Suit is a suit equipped with a camera, Global Navigation Satellite System (GNSS), and various other sensors, to be worn by search and rescue (SAR) dogs for the purpose of enhancing SAR dog operations. This paper presents an image recognition system for use in disaster scenarios and its integration with the Cyber‐Enhanced Canine Suit platform. The system’s intended use is to spot personal items of missing individuals or other visual clues in video streams from various disaster response platforms. The system facilitates quick learning of targets from limited data and makes providing that data quick and easy. It also provides backtrack recognition functionality, to rapidly find novel targets in the seen footage. We evaluated the recognition system on footage gathered in the field, obtaining promising results. Integrated with the Cyber‐Enhanced Canine Suit, the system can automatically plot detections of search targets onto a map display, to provide operators with a quick overview of what was seen where.     

Development of a search and rescue robot system for the underground building environment

The underground building environment plays an increasingly important role in the construction of modern cities. To deal with possible fires, collapses, and so on, in underground building space, it is a general trend to use rescue robots to replace humans. This paper proposes a dual-robot system solution for search and rescue in an underground building environment. To speed up rescue and search, the two robots focus on different tasks. However, the environmental perception information and location of them are shared. The primary robot is used to quickly explore the environment in a wide range, identify objects, cross difficult obstacles, and so on. The secondary robot is responsible for grabbing, carrying items, clearing obstacles, and so on. In response to the difficulty of rescue caused by unknown scenes, the Lidar, inertial measurement unit and multiview cameras are integrated for large-scale 3D environment mapping. The depth camera detects the objects to be rescued and locate them on the map. A six-degree-of-freedom manipulator with a two-finger gripper is equipped to open doors and clear roadblocks during the rescue. To solve the problem of severe signal attenuation caused by reinforced concrete walls, corners and long-distance transmission, a wireless multinode networking solution is adopted. In the case of a weak wireless signal, the primary robot uses autonomous exploration for environmental perception. Experimental results show the robots' system has high reliability in over-the-horizon maneuvering, teleoperation of the door opening and grasping, object searching, and environmental perception, and can be well applied to underground search and rescue.     

A hybrid approach for search and rescue using 3DCNN and PSO

Neural Computing and Applications - Search and rescue are essential applications of disaster management in which people are evacuated from the disaster-prone area to a safer place. This overall...     

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.     

基于手机信号定位的地震灾害搜救系统研究

基于对手机信号定位技术的研究,设计了一种地震灾害中浅掩埋人员的搜救定位系统。采用TDOA/AOA混合定位算法进行两次定位,主动诱发并探测被掩埋人员随身携带手机发射的信号对该人员进行定位。阐述了系统应用到的关键技术,并对TDOA/AOA混合定位算法在实际搜救环境下进行了仿真,验证了采用该算法的可行性。该系统为灾害救援中的人员定位难题提供了一种新的解决方案,使得对大面积灾害现场被掩埋人员准确而快速的定位成为可能。