In this paper, we consider two coupled problems for distributed multi-robot systems (MRSs) coordinating with limited field of view (FOV) sensors: adaptive tuning of interaction gains and rejection of sensor attacks. First, a typical shortcoming of distributed control frameworks (e.g., potential fields) is that the overall system behavior is highly sensitive to the gain assigned to relative interactions. Second, MRSs with limited FOV sensors can be more susceptible to sensor attacks aimed at their FOVs, and therefore must be resilient to such attacks. Based on these shortcomings, we propose a comprehensive solution that combines efforts in adaptive gain tuning and attack resilience to the problem of topology control for MRSs with limited FOVs. Specifically, we first derive an adaptive gain tuning scheme based on satisfying nominal pairwise interactions, which yields a dynamic balancing of interaction strengths in a robot's neighborhood. We then model additive sensor and actuator attacks (or faults) and derive H infinity control protocols by employing a static output-feedback technique, guaranteeing bounded L2 gains of the error induced by the attack (fault) signals. Finally, simulation results using ROS Gazebo are provided to support our theoretical findings.
Università degli Studi di Modena e Reggio Emilia（摩德纳和雷焦艾米利亚大学）；Polytechnique Montréal（蒙特利尔综合理工学院）；Instituto Tecnológico de Aeronáutica（巴西航空技术学院）
Efficient networking of many-robot systems is considered one of the grand challenges of robotics. In this article, we address the problem of achieving resilient, dynamic interconnection topologies in multi-robot systems. In scenarios in which the overall network topology is constantly changing, we aim at avoiding the onset of single points of failure, particularly situations in which the failure of a single robot causes the loss of connectivity for the overall network. We propose a method based on the combination of multiple control objectives and we introduce an online distributed optimizatio…
The task of maximizing coverage using multiple robots has several applications such as surveillance, exploration, and environmental monitoring. A major challenge of deploying such multi-robot systems in a practical scenario is to ensure resilience against robot failures. A recent work introduced the Resilient Coverage Maximization (RCM) problem where the goal is to maximize a submodular coverage utility when the robots are subject to adversarial attacks or failures. The RCM problem is known to be NP-hard. In this paper, we propose two approximation algorithms for the RCM problem, namely, the O…
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots base…
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots base…
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Department of Electrical and Computer Engineering, Drexel University（电子与计算机工程系，德雷塞尔大学）；GRASP Lab, University of Pennsylvania（GRASP实验室，宾夕法尼亚大学）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（机器人与智能机器研究所，佐治亚理工学院）；Department of Computer Science, University of Southern California（计算机科学系，南加州大学）
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…
The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi-Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary prospection and mapping. Based on Robot Operating System version 2 (ROS 2) and enhanced with Visual Simultaneous Localisation And Mapping (vSLAM) for map generation, REALMS2 uses a mesh network for a robust ad hoc network. A single graphica…
Failure-Aware Multi-Robot Coordination for Resilient and Adaptive Target Tracking
Department of Electrical and Computer Engineering, Drexel University（德雷塞尔大学电气与计算机工程系）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；GRASP Lab, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）
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…
University of Pennsylvania（宾夕法尼亚大学）；Lehigh University（利哈伊大学）
For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities…
Multi-robot systems face challenges in reducing human interventions as they are often deployed in dangerous environments. It is therefore necessary to include a methodology to assess robot failure rates to reduce the requirement for costly human intervention. A solution to this problem includes robots with the ability to work together to ensure mission resilience. To prevent this intervention, robots should be able to work together to ensure mission resilience. However, robotic platforms generally lack built-in interconnectivity with other platforms from different vendors. This work aims to ta…
Self-Organizing Aerial Swarm Robotics for Resilient Load Transportation : A Table-Mechanics-Inspired Approach
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（乔治亚理工学院机器人与智能机器研究所）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；Combat Capabilities Development Command, Army Research Laboratory (CCDC ARL)（陆军研究实验室作战能力发展司令部（CCDC ARL））
In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Towards this end, the task prioritization and execution -- through which the allocation of tasks to robots is effectively realized -- are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is int…
University of Cambridge（剑桥大学）；University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Southern California（南加利福尼亚大学）；Amazon（亚马逊）；Army Research Laboratory（陆军研究实验室）
Robustness is key to engineering, automation, and science as a whole. However, the property of robustness is often underpinned by costly requirements such as over-provisioning, known uncertainty and predictive models, and known adversaries. These conditions are idealistic, and often not satisfiable. Resilience on the other hand is the capability to endure unexpected disruptions, to recover swiftly from negative events, and bounce back to normality. In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by …
Universidad Complutense de Madrid（马德里康普顿斯大学）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））；School of Robotics, Hunan University（湖南大学机器人学院）
University of Pennsylvania（宾夕法尼亚大学）；University of Michigan（密歇根大学）
Emerging applications of collaborative autonomy, such as Multi-Target Tracking, Unknown Map Exploration, and Persistent Surveillance, require robots plan paths to navigate an environment while maximizing the information collected via on-board sensors. In this paper, we consider such information acquisition tasks but in adversarial environments, where attacks may temporarily disable the robots' sensors. We propose the first receding horizon algorithm, aiming for robust and adaptive multi-robot planning against any number of attacks, which we call Resilient Active Information acquisitioN (RAIN).…
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院与州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Maryland（马里兰大学）
In this letter, we consider a distributed submodular maximization problem for multi-robot systems when attacked by adversaries. One of the major challenges for multi-robot systems is to increase resilience against failures or attacks. This is particularly important for distributed systems under attack as there is no central point of command that can detect, mitigate, and recover from attacks. Instead, a distributed multi-robot system must coordinate effectively to overcome adversarial attacks. In this work, our distributed submodular action selection problem models a broad set of scenarios whe…
Trust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Whiting School of Engineering at the Johns Hopkins University（约翰斯·霍普金斯大学惠廷工程学院）
In the context of constraint-driven control of multi-robot systems, in this paper, we propose an optimization-based framework that is able to ensure resilience and energy-awareness of teams of robots. The approach is based on a novel, frame-theoretic, measure of resilience which allows us to analyze and enforce resilient behaviors of multi-robot systems. The properties of resilience and energy-awareness are encoded as constraints of a convex optimization program which is used to synthesize the robot control inputs. This allows for the combination of such properties with the execution of coordi…
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
In this paper, we solve a multi-robot informative path planning (MIPP) task under the influence of uncertain communication and adversarial attackers. The goal is to create a multi-robot system that can learn and unify its knowledge of an unknown environment despite the presence of corrupted robots sharing malicious information. We use a Gaussian Process (GP) to model our unknown environment and define informativeness using the metric of mutual information. The objectives of our MIPP task is to maximize the amount of information collected by the team while maximizing the probability of resilien…
Virginia Tech（弗吉尼亚理工大学）；University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）
The problem of target tracking with multiple robots consists of actively planning the motion of the robots to track the targets. A major challenge for practical deployments is to make the robots resilient to failures. In particular, robots may be attacked in adversarial scenarios, or their sensors may fail or get occluded. In this paper, we introduce planning algorithms for multi-target tracking that are resilient to such failures. In general, resilient target tracking is computationally hard. Contrary to the case where there are no failures, no scalable approximation algorithms are known for …
National Cheng Kung University（成功大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
This paper investigates the resilient control, analysis, recovery, and operation of mobile robot networks in time-varying formation tracking under deception attacks on global positioning. Local and global tracking control algorithms are presented to ensure redundancy of the mobile robot network and to retain the desired functionality for better resilience. Lyapunov stability analysis is utilized to show the boundedness of the formation tracking error and the stability of the network under various attack modes. A performance index is designed to compare the efficiency of the proposed formation …
This paper proposes KC-TSS: K-Clustered-Traveling Salesman Based Search, a failure resilient path planning algorithm for heterogeneous robot teams performing target search in human environments. We separate the sample path generation problem into Heterogeneous Clustering and multiple Traveling Salesman Problems. This allows us to provide high-quality candidate paths (i.e. minimal backtracking, overlap) to an Information-Theoretic utility function for each agent. First, we generate waypoint candidates from map knowledge and a target prediction model. All of these candidates are clustered accord…
VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards
Department of Electronics and Telecommunications, Politecnico di Torino（电子与电信系，托斯纳大学）；PoliTO Interdepartmental Centre for Service Robotics (PIC4SeR)（PoliTO 服务机器人跨部门中心（PIC4SeR））
Polytechnique Montréal（蒙特利尔综合理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop clo…
University of Pennsylvania（宾夕法尼亚大学）；Drexel University（德雷塞尔大学）
Consensus algorithms form the foundation for many distributed algorithms by enabling multiple robots to converge to consistent estimates of global variables using only local communication. However, standard consensus protocols can be easily led astray by non-cooperative team members. As such, the study of resilient forms of consensus is necessary for designing resilient distributed algorithms. W-MSR consensus is one such resilient consensus algorithm that allows for resilient consensus with only local knowledge of the communication graph and no a priori model for the data being shared. However…
A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators
Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Institute of Materials Science, University of Connecticut（康涅狄格大学材料科学研究所）；School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut（康涅狄格大学机械、航空航天与制造工程学院）；Material Science and Engineering, University of Connecticut（康涅狄格大学材料科学与工程系）；Inorganic Polymers Department, Petru Poni Institute of Macromolecular Chemistry（彼得·波尼宏分子化学研究所无机聚合物部门）
Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications. To address this issue, this work first investigates the dissipative nature of flying robots considering aerial drag effects an…
Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This paper focuses on an emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. To this end, we introduce a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflect…
The paper focuses on collision-inclusive motion planning for impact-resilient mobile robots. We propose a new deformation recovery and replanning strategy to handle collisions that may occur at run-time. Contrary to collision avoidance methods that generate trajectories only in conservative local space or require collision checking that has high computational cost, our method directly generates (local) trajectories with imposing only waypoint constraints. If a collision occurs, our method then estimates the post-impact state and computes from there an intermediate waypoint to recover from the …
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院智能系统与控制研究所）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Osnabrück University（奥斯纳布吕克大学）；Joint Lab for AI & DS（人工智能与数据科学联合实验室）；Institute of Computer Science（计算机科学学院）
Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing
School of Artificial Intelligence, Harbin Institute of Technology（哈尔滨工业大学人工智能学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Pengcheng Laboratory（鹏城实验室）；Suzhou Research Institute, Harbin Institute of Technology（哈尔滨工业大学苏州研究院）
This paper considers the problem of assigning multiple mobile robots to goals on transport networks with uncertain information about travel times. Our aim is to produce optimal assignments, such that the average waiting time at destinations is minimized. Since noisy travel time estimates result in sub-optimal assignments, we propose a method that offers resilience to uncertainty by making use of redundant robots. However, solving the redundant assignment problem optimally is strongly NP-hard. Hence, we exploit structural properties of our mathematical problem formulation to propose a polynomia…
NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；California Institute of Technology（加州理工学院）；Santa Clara University（圣克拉拉大学）；Polytechnic University of Bari（巴里理工大学）
Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable navigation. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop c…
University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）
Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail, resulting in the withdrawal of robots from the collective task, and consequently the inability of the remaining active robots to coordinate with each other. As a result, traditional design paradigms become insufficient and, in contrast, resilient designs against system-wide fai…
