A Multirobot System for Distributed Area Coverage and Signal Searching in Large Outdoor Scenarios*

This work presents a complete multirobot solution for signal searching tasks in large outdoor scenarios. An evaluation of two different coverage path‐planning strategies according to field size and shape is presented. A signal location system developed to simulate mines or chemical source detections is also described. The solution presented is a pioneer in evaluating multimaster robotics operative system architectures with a fleet of robots in real scenarios. This solution minimizes the use of communications bandwidth required for full operation. Finally, field results are provided, and the advantages of the implemented solution are analyzed.     

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.     

Applying a path planner based on RRT to cooperative multirobot box-pushing

Considering robot systems in the real world, a multirobot system where multiple robots work simultaneously without colliding with each other is more practical than a single-robot system where only one robot works. Therefore, solving the path-planning problem in a multirobot system is very important. In this study, we developed a path-planner based on the rapidly exploring random tree (RRT), which is a data structure and algorithm designed for efficiently searching for multirobot box-pushing, and made experiments in real environments. A path planner must construct a plan which avoids the robot colliding with obstacles or with other robots. Moreover, in some cases, a robot must collaborate with other robots to transport the box without colliding with any obstacles. Our proposed path planner constructs a box-transportation plan and the path plans of each robot bearing the above considerations in mind. Experimental results showed that our proposed planner can construct a multirobot box-pushing plan without colliding with obstacles, and that the robots can execute tasks according to the plan in real environments. We also checked that multiple robots can perform problem tasks when only one robot could not transport the box to the goal. This work was presented in part at the 13th International Symposium on Articifial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Robust Navigation and Seamless Localization for Carlike Robots in Indoor‐outdoor Environments

This paper describes a navigation and seamless localization system that permits carlike robots to move safely in heterogeneous scenarios within indoor and outdoor environments. The robot localization integrates different sensor (GPS, odometry, laser rangefinders) information depending on the kind of area (indoors, outdoors, and areas between) or on the sensor uncertainty in such a way that there are no discontinuities in the localization, and a bounded uncertainty is constantly maintained. Transitions through indoor and outdoor environments are thoroughly considered to assure a smooth change in‐between. The paper addresses a navigation technique that combines two well‐known obstacle avoidance techniques, namely the nearness diagram and the dynamic window approaches, exploiting the advantages and properties of both, and integrating the seamless localization technique. The navigation technique is developed for carlike robots by considering their shape and kinodynamic constraints, and the restrictions imposed by the environment. Forward‐backward maneuvers are also integrated in the method, allowing difficult situations in dense scenarios to be managed. The whole system has been tested in simulations and experiments in real large‐scale scenarios.     

Current research in multirobot systems

As research progresses in distributed robotic systems, more and more aspects of multirobot systems are being explored. This article describes advances in multirobot systems, and surveys the current state of the art. The focus is principally on research that has been demonstrated in physical robot implementations. I have identified eight primary research topics within multirobot systems—biological inspirations, communication, architectures, localization/mapping/exploration, object transport and manipulation, motion coordination, reconfigurable robots, and learning—and discuss the current state of research in these areas. As I describe each research area, I identify some key open issues in multirobot team research, and conclude by identifying several additional open research issues in distributed mobile robotic systems. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.     

Relational Model for Robotic Semantic Navigation in Indoor Environments

The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks.     

Local communication of multiple mobile robots: Design of optimal communication area for cooperative tasks

This paper presents an optimal design for local communication between multiple mobile robots. In previous studies of local communication in multirobot systems, the area of communication was not designed using mathematical analysis, but only time-consuming simulations of multirobot communications. We analyzed the information transmission efficiency and created an optimal communication area that minimizes the information transmission time to multiple robots. This optimization comprises two steps. First, we derive the “information transmission probability” for various task models. Next, the derived information transmission probability is used to minimize the information transmission time. The optimal communication design is tested for various tasks, using system parameters. The analytical results are further verified by using computer simulations of multirobot communications and experiments with local communication. © 1998 John Wiley & Sons, Inc.     

Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

A Neural Network Approach to Dynamic Task Assignment of Multirobots

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

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.     

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post‐disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human‐swarm interfaces. All these aspects have been analyzed with respect to the state‐of‐the‐art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real‐time intervention.     

基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

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.     

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.     

基于多智能体深度强化学习的协作导航应用

多机器人协作导航目前广泛应用于搜索救援、物流等领域, 协作策略与目标导航是多机器人协作导航面临的主要挑战. 为提高多个移动机器人在未知环境下的协作导航能力, 本文提出了一种新的分层控制协作导航(hierarchical control cooperative navigation, HCCN) 策略, 利用高层目标决策层和低层目标导航层, 为每个机器人分配一个目标点, 并通过全局路径规划和局部路径规划算法, 引导智能体无碰撞地到达分配的目标点. 通过Gazebo平台进行实验验证, 结果表明, 文中所提方法能够有效解决协作导航过程中的稀疏奖励问题, 训练速度至少可提高16.6%, 在不同环境场景下具有更好的鲁棒性, 以期为进一步研究多机器人协作导航提供理论指导, 应用至更多的真实场景中.     

动态环境下分布式异构多机器人避障方法研究

多机器人系统在联合搜救、智慧车间、智能交通等领域得到了日益广泛的应用。目前,多个机器人之间、机器人与动态环境之间的路径规划和导航避障仍需依赖精确的环境地图,给多机器人系统在非结构环境下的协调与协作带来了挑战。针对上述问题,本文提出了不依赖精确地图的分布式异构多机器人导航避障方法,建立了基于深度强化学习的多特征策略梯度优化算法,并考虑了人机协同环境下的社会范式,使分布式机器人能够通过与环境的试错交互,学习最优的导航避障策略;并在Gazebo仿真环境下进行了最优策略的训练学习,同时将模型移植到多个异构实体机器人上,将机器人控制信号解码,进行真实环境测试。实验结果表明:本文提出的多特征策略梯度优化算法能够通过自学习获得最优的导航避障策略,为分布式异构多机器人在动态环境下的应用提供了一种技术参考。     

A programming environment for real-time control of distributed multiple robotic systems

In recent years there has been great interest in robot software control architectures. However, although many interesting solutions have been presented, most of the research problems tackled related to a single robot perception, navigation and action in everyday environments. Instead, most of the practical applications of mobile robotics for service tasks in civilian environments consist of systems composed of multiple robots communicating with each other, with external sensing and actuating devices, and with external supervising workstations. RoboCup offers a great opportunity to deal with this problem. In fact the software architecture of a robot soccer player must allow successful intra-robot integration of the different activities (visual perception, path planning, strategy planning, motion control, etc.) spanning many different types of representation (raw sensor data, images, symbolic plans, etc.) and it must also guarantee successful inter-robot integration by supporting communication and cooperation. This paper focuses on this problem, presenting ETHNOS-IV - a programming environment for the design of a real-time control system composed of different robots, devices and external supervising or control stations - which has been successfully used within the Italian ART robot team in the RoboCup-99 competition. ETHNOS provides support from three main point of views which will be addressed in detail: inter-robot and intra-robot communication, realtime task scheduling, and software engineering and code reuse. Experimental results illustrating the advantages of this approach will also be presented.     

A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving toward individual goals within a common environment. Most popular algorithms for multirobot planning manage the complexity of the problem by planning trajectories for robots individually; such decoupled methods are not guaranteed to find a solution if one exists. In contrast, this paper describes a multiphase approach to the planning problem that uses a graph and spanning tree representation to create and maintain obstacle-free paths through the environment for each robot to reach its goal. The resulting algorithm guarantees a solution for a well-defined number of robots in a common environment. The computational cost is shown to be scalable with complexity linear in the number of the robots, and demonstrated by solving the planning problem for 100 robots, simulated in an underground mine environment, in less than 1.5 s with a 1.5 GHz processor. The practicality of the algorithm is demonstrated in a real-world application requiring coordinated motion planning of multiple physical robots.     

Multi-robot olfactory search in structured environments

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.