Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. However, in harsh environments with no existing communication infrastructure, robots must form ad-hoc networks, forcing the team to operate in a distributed fashion. To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN). Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. To do this, agents glean information about the topology of the network from packet transmissions and feed it to a GNN running locally which instructs the agent when and where to transmit the latest state information. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions. Our approach performs favorably compared to industry-standard methods for data distribution such as random flooding and round robin. We also show that the trained policies generalize to larger teams of both static and mobile agents.
University of Cambridge（剑桥大学）；Department of Computer Science and Technology（计算机科学与技术系）
This paper shows how Graph Neural Networks can be used for learning distributed coordination mechanisms in connected teams of robots. We capture the relational aspect of robot coordination by modeling the robot team as a graph, where each robot is a node, and edges represent communication links. During training, robots learn how to pass messages and update internal states, so that a target behavior is reached. As a proxy for more complex problems, this short paper considers the problem where each robot must locally estimate the algebraic connectivity of the team's network topology.
We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member's sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots mus…
We examine three different algorithms that enable the collision certificate method from [Bialkowski, et al.] to handle the case of a centralized multi-robot team. By taking advantage of symmetries in the configuration space of multi-robot teams, our methods can significantly reduce the number of collision checks vs. both [Bialkowski, et al.] and standard collision checking implementations.
At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative me…
Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce …
Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach
University of Pennsylvania（宾夕法尼亚大学）；Amazon Robotics（亚马逊机器人公司）；Universidad Santo Tomas（圣托马斯大学）；Bethune Cookman University（白求恩-库克曼大学）；Lehigh University（利哈伊大学）
Stanford University（斯坦福大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Massachusetts Institute of Technology（麻省理工学院）
Communication connectivity is desirable for safe and efficient operation of multi-robot systems. While decentralized algorithms for connectivity maintenance have been explored in recent literature, the majority of these works do not account for robot motion and sensing uncertainties. These uncertainties are inherent in practical robots and result in robots deviating from their desired positions which could potentially result in a loss of connectivity. In this paper we present a Decentralized Connectivity Maintenance algorithm accounting for robot motion and sensing Uncertainties (DCMU). We fir…
Colorado School of Mines（科罗拉多矿业学院）；U.S. Army Research Laboratory（美国陆军研究实验室）
Multi-robot systems of increasing size and complexity are used to solve large-scale problems, such as area exploration and search and rescue. A key decision in human-robot teaming is dividing a multi-robot system into teams to address separate issues or to accomplish a task over a large area. In order to address the problem of selecting teams in a multi-robot system, we propose a new multimodal graph embedding method to construct a unified representation that fuses multiple information modalities to describe and divide a multi-robot system. The relationship modalities are encoded as directed g…
Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tasks, has been widely used for complex and large-scale scenarios, including disaster search and rescue, site surveillance, and social security. However, due to the variety of the task requirements, it is challenging to accurately compose a robot team with appropriate sizes and functions to dynamically satisfy task needs while limiting the robot resource cost to a low level. To solve this problem, in this paper, a novel…
Trust has been identified as a central factor for effective human-robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human agents and multiple robotic agents. To fill this research gap, we present the trust inference and propagation (TIP) model for trust modeling in multi-human multi-robot teams. We assert that in a multi-human multi-robot team, there exist two types of experiences that any human agent has …
Polytechnique Montréal（蒙特利尔综合理工大学）；European Space Agency（欧洲空间局）；Università degli Studi di Modena e Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Redundancy and parallelism make decentralized multi-robot systems appealing solutions for the exploration of extreme environments. However, effective cooperation often requires team-wide connectivity and a carefully designed communication strategy. Several recently proposed decentralized connectivity maintenance approaches exploit elegant algebraic results drawn from spectral graph theory. Yet, these proposals are rarely taken beyond simulations or laboratory implementations. In this work, we present two major contributions: (i) we describe the full-stack implementation---from hardware to soft…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Carnegie Mellon University（卡内基梅隆大学）
In this paper, we consider a team of mobile robots executing simultaneously multiple behaviors by different subgroups, while maintaining global and subgroup line-of-sight (LOS) network connectivity that minimally constrains the original multi-robot behaviors. The LOS connectivity between pairwise robots is preserved when two robots stay within the limited communication range and their LOS remains occlusion-free from static obstacles while moving. By using control barrier functions (CBF) and minimum volume enclosing ellipsoids (MVEE), we first introduce the LOS connectivity barrier certificate …
Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams. Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action…
This research proposes new tools for investigation of behavioral diversity in multi-robot systems and a significant body of results using these tools in simulated and real mobile robot experiments. The experiments specifically describe a framework of defining behavior-based strategies for multi-robot tasks as robot foraging, robot soccer and robot formation. The research focuses specifically on motor schema-based multi-robot systems, which are an important example of behavior-based control.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Stanford University（斯坦福大学）
Inter-robot communication enables multi-robot systems to coordinate and execute complex missions efficiently. Thus, maintaining connectivity of the communication network between robots is essential for many multi-robot systems. In this paper, we present a trajectory planner for connectivity maintenance of a multi-robot system. We first define a weighted undirected graph to represent the connectivity of the system. Unlike previous connectivity maintenance works, we explicitly account for robot motion and sensing uncertainties while formulating the graph edge weights. These uncertainties result …
Massachusetts Institute of Technology（麻省理工学院）；University of Pennsylvania（宾夕法尼亚大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
This paper considers the problem of planning trajectories for a team of sensor-equipped robots to reduce uncertainty about a dynamical process. Optimizing the trade-off between information gain and energy cost (e.g., control effort, distance travelled) is desirable but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planning algorithms based on techniques such as coordinate descent lose their performance guarantees. Methods based on local search provide performance guarantees for optimizing a non-monotone submodular function, but requi…
UNSW Sydney（新南威尔士大学悉尼分校）；Data61, CSIRO（联邦科学与工业研究组织Data61）；Defence Science and Technology Group（国防科学与技术集团）
Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research progress in these directions. We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surr…
Connectivity maintenance plays a key role in achieving a desired global behavior among a swarm of robots. However, connectivity maintenance in realistic environments is hampered by lack of computation resources, low communication bandwidth, robot failures, and unstable links. In this paper, we propose a novel decentralized connectivity-preserving algorithm that can be deployed on top of other behaviors to enforce connectivity constraints. The algorithm takes a set of targets to be reached while keeping a minimum number of redundant links between robots, with the goal of guaranteeing bandwidth …
This paper presents a framework for monitoring human and robot conditions in human multi-robot interactions. The proposed framework consists of four modules: 1) human and robot conditions monitoring interface, 2) synchronization time filter, 3) data feature extraction interface, and 4) condition monitoring interface. The framework is based on Robot Operating System (ROS), and it supports physiological and behavioral sensors and devices and robot systems, as well as custom programs. Furthermore, it allows synchronizing the monitoring conditions and sharing them simultaneously. In order to valid…
Service Robotics LaboratoryUniversidad Pablo de Olavide（塞维利亚大学服务机器人实验室）；Computer Vision and Aerial Robotics GroupCentre for Automation and Robotics (C.A.R.)Universidad Politécnica de Madrid (UPM-CSIC)（马德里理工大学（UPM-CSIC）自动化与机器人中心计算机视觉与空中机器人小组）；Universidad Pablo de Olavide（塞维利亚大学）；Universidad Politécnica de Madrid (UPM-CSIC)（马德里理工大学（UPM-CSIC））；Centre for Automation and Robotics (C.A.R.)（自动化与机器人中心（C.A.R.））；Computer Vision and Aerial Robotics Group（计算机视觉与空中机器人小组）；Service Robotics Laboratory（服务机器人实验室）
The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and U…
Institute of Parallel and Distributed Systems, University of Stuttgart（斯图加特大学并行与分布式系统研究所）
In this work we consider three different cases of robot-robot interactions and resulting global information transfer in robot swarms. These mechanisms define cooperative properties of the system and can be used for designing collective behavior. These three cases are demonstrated and discussed based on experiments in a swarm of microrobots "Jasmine".
In many cases the multi-robot systems are desired to execute simultaneously multiple behaviors with different controllers, and sequences of behaviors in real time, which we call \textit{behavior mixing}. Behavior mixing is accomplished when different subgroups of the overall robot team change their controllers to collectively achieve given tasks while maintaining connectivity within and across subgroups in one connected communication graph. In this paper, we present a provably minimum connectivity maintenance framework to ensure the subgroups and overall robot team stay connected at all times …
Hong Kong University of Science and Technology（香港科技大学）
In this paper, we propose a coverage control system for a multi-robot team with heterogeneous capabilities to patrol or monitor a bounded environment. The capability could be defined as any criterion of robots like remaining power or mobile speed, depending on the purpose. The proposed control system aims to allocate different portions of the environment to the robots according to their capabilities, i.e., the robot with higher capability takes a larger portion of the environment while the robot with lower capability takes a smaller one. We use the block coordinate descent (BCD) method to opti…
Georgia Institute of Technology（佐治亚理工学院）；CCDC Army Research Laboratory（美国陆军研究实验室）
For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the ta…
In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of units per category representing heterogeneous capabilities. Every task area with different importance demands a total number of units contributed by all of the robots within its area. Moreover, we assume the importance and the total number o…
Georgia Institute of Technology（佐治亚理工学院）；Georgia Tech Research Institute（佐治亚理工研究院）；UBTECH North America Research and Development Center Corporation（优必选北美研发中心有限公司）
This paper takes the first step towards a reactive, hierarchical multi-robot task allocation and planning framework given a global Linear Temporal Logic specification. The capabilities of both quadrupedal and wheeled robots are leveraged via a heterogeneous team to accomplish a variety of navigation and delivery tasks. However, when deployed in the real world, all robots can be susceptible to different types of disturbances, including but not limited to locomotion failures, human interventions, and obstructions from the environment. To address these disturbances, we propose task-level local an…
University of Southern California（南加州大学）；Georgia Institute of Technology（佐治亚理工学院）
We propose a framework for resilience in a networked heterogeneous multi-robot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable (\eg a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually est…
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Shanghai Jiao Tong University（上海交通大学）；Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）；Genisom AI；University of Bristol（布里斯托大学）；Purdue University（普渡大学）；Newcastle University（新castle大学）
Institute of Networked and Embedded Systems, University of Klagenfurt（克拉根福大学网络化与嵌入式系统研究所）
Mobile robots, especially unmanned aerial vehicles (UAVs), are of increasing interest for surveillance and disaster response scenarios. We consider the problem of multi-robot persistent surveillance with connectivity constraints where robots have to visit sensing locations periodically and maintain a multi-hop connection to a base station. We formally define several problem instances closely related to multi-robot persistent surveillance with connectivity constraints, i.e., connectivity-constrained multi-robot persistent surveillance (CMPS), connectivity-constrained multi-robot reachability (C…
Polytechnique Montreal（蒙特利尔理工学院）；École de Technologie Supérieure（高等技术学院）
The coordination of robot swarms - large decentralized teams of robots - generally relies on robust and efficient inter-robot communication. Maintaining communication between robots is particularly challenging in field deployments. Unstructured environments, limited computational resources, low bandwidth, and robot failures all contribute to the complexity of connectivity maintenance. In this paper, we propose a novel lightweight algorithm to navigate a group of robots in complex environments while maintaining connectivity by building a chain of robots. The algorithm is robust to single robot …
MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
FZI Forschungszentrum für Informatik, Karlsruhe, Germany（弗赖堡信息研究中心，卡尔斯鲁厄，德国）；Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland（机器人系统实验室（RSL），苏黎世联邦理工学院，苏黎世，瑞士）；Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany（机器智能与机器人实验室（MaiRo），卡尔斯鲁厄技术学院（KIT），卡尔斯鲁厄，德国）；European Space Agency (ESA), Noordwijk, Netherlands（欧洲航天局（ESA），诺德威克，荷兰）
For a team of heterogeneous robots executing multiple tasks, we propose a novel algorithm to optimally allocate tasks to robots while accounting for their different capabilities. Motivated by the need that robot teams have in many real-world applications of remaining operational for long periods of time, we allow each robot to choose tasks taking into account the energy consumed by executing them, besides the global specifications on the task allocation. The tasks are encoded as constraints in an energy minimization problem solved at each point in time by each robot. The prioritization of a ta…
Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming
Johns Hopkins University Applied Physics Laboratory（约翰霍普金斯大学应用物理实验室）；Department of Mechanical Engineering, Johns Hopkins University（约翰霍普金斯大学机械工程系）；DEVCOM Army Research Laboratory（国防部陆军研究实验室）
University of Nevada, Reno（内华达大学里诺分校）；NTNU（挪威科技大学）；ETH Zurich（苏黎世联邦理工学院）
This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying rob…
University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfill the mission, which directly feed into the next stage of the…
Humans can selectively focus on different information based on different tasks requirements, other people's abilities and availability. Therefore, they can adapt quickly to a completely different and complex environments. If, like people, robot could obtain the same abilities, then it would greatly increase their adaptability to new and unexpected situations. Recent efforts in Heterogeneous Multi Robots Teaming have try to achieve this ability, such as the methods based on communication and multi-modal information fusion strategies. However, these methods will not only suffer from the exponent…
