Team Of Robots Research Articles

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

Control of a drone swarm as a unity requires decentralization and hierarchy. Decentralizing control of drone swarms is necessary to free the human operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that human operators can adjust the activity of the swarm as a unit. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human operator. The control hierarchy consists of a human operator who controls the change in the behavior of the drone coordinator. The drone coordinator controls the changes in the programmed behavior of individual drones. This approach is an analog of the management of human workers who perform assigned work, which opens up several possibilities. First, it is possible to use formal models of performance people’s behavior in social teams. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity

Emergent memory from tapping collisions in active granular matter

In an equilibrium thermal environment, random elastic collisions between background particles and a tracer establish the picture of Brownian motion fulfilling the celebrated Einstein relation between diffusivity and mobility. However, extensions of the Einstein relation to link dissipation, fluctuations, and nonequilibrium dynamical mechanisms in active matter systems are still debated. Here, we investigate experimentally the impact of an active background on a passive tracer using vibrationally excited active particles, that result in multiple correlated tapping collisions with the tracer, for which a persistent memory emerges in the dynamics. The system is described by a generalized active Einstein relation that constrains fluctuations, dissipation, and effective activity, by taking the emerging tracer memory into account. Since the resulting persistence can largely be tuned by the environmental density and motility, our findings can be useful to engineer properties of various active systems in biomedical applications, microfluidics, chemical engineering, or swarm robotics.

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

A heterogeneous robots collaboration for safety, security, and rescue robotics: e-ASIA joint research program for disaster risk and reduction management

In the face of humanitarian crises such as torrential rainfall, resulting floods, and landslides, rapid rescue operations are often imperative. However, owing to the inherent dangers and unpredictability of such circumstances, immediate on-site aid delivery is frequently unfeasible. In such challenging scenarios, mobile robot systems have emerged as the optimal solution for aiding search and rescue efforts. The 'Informational system for management of flood and landslide disaster areas using a distributed heterogeneous robotic team' project, initiated by the International e-ASIA Joint Research Program, united research teams from Japan, Russia, and Thailand, each contributing unique expertize and experience towards common objectives. Drawing upon our extensive theoretical and practical knowledge in disaster response research, we developed an integrated international operational framework for disaster site management, centered on dispersed, heterogeneous semi-autonomous Unmanned Aerial Vehicles (UAVs), Semi-Autonomous Rough Terrain Vehicles (UGVs), and Semi-Autonomous Surface Vehicles (USVs). This paper provides an overview of the methodologies, models, and algorithms employed throughout the successful three-year project conducted by the Thailand team while also directing interested readers to supplementary research papers for in-depth insights.

Use of cross‐training in human–robot collaborative rescue

AbstractHuman–robot collaboration has been widely used in postdisaster investigation and rescue. Human–robot team training is a good way to improve the team rescue efficiency and safety; two common training methods, namely, procedural training and cross‐training, are explored in this study. Currently, relatively few studies have explored the impact of cross‐training on human–robot collaboration in rescue tasks. Cross‐training will be novel to most rescuers and as such, an evaluation of cross‐training in comparison with more conventional procedural training is warranted. This study investigated the effects of these two training methods on rescue performance, situation awareness and workload. Forty‐two participants completed a path‐planning and a photo‐taking task in an unfamiliar simulated postdisaster environment. The rescue performance results showed that cross‐training method had significant advantages over procedural training for human–robot collaborative rescue tasks. During the training process, compared with procedural training, participants were more likely to achieve excellent photo‐taking performance after cross‐training; after training, the length of the route planned by the cross‐training group was significantly shorter than that of the procedural‐training group. In addition, procedural‐training marginal significantly increased the emotion demand, which proves that cross‐training can well control the emotions of the operators and make them more involved in the rescue task. The study also found that arousal level increased significantly after the first cross‐training session, and decreased to the same level as procedural training after multiple sessions. These results contribute to the application of cross‐training in human–robot collaborative rescue teams.

Impact of initialization of a modified particle swarm optimization on cooperative source searching

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.

Corrections to “UWB-VIO Fusion for Accurate and Robust Relative Localization of Round Robotic Teams”

Corrections to “UWB-VIO Fusion for Accurate and Robust Relative Localization of Round Robotic Teams”

Overcoming adversaries in multirobot navigation.

A path-planning algorithm allows safe navigation of robot teams in cluttered environments while in the presence of adversaries.

Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.

Development and Review of Group Rescue Robots Based on Artificial Intelligence Technology

Rescue robots can perform rescue missions in dangerous and complex environments, protect humans from harm, and improve the efficiency and effectiveness of rescue, thus playing an increasingly important role in disaster management and rescue. This article reviews the technologies and methods required to apply artificial intelligence to rescue robot teams. Firstly, the feasibility of motion control for swarm robots was explored from the perspective of biomimetic robots. Through the analysis of animal biomimetics and the comparison of commonly used topological structures, the nature of team rescue robot rescue is emphasized, and based on this, a scheme for optimizing topological networks by combining environmental intelligence is proposed. Secondly, several existing micro robots were introduced and their data loading capabilities were evaluated. On this basis, the process of robot vision and motion commands was outlined. At the meanwhile, researchers focus on the current mainstream robot motion trajectory algorithms, and study the algorithm optimization process from extending the motion path planning of a single robot to group coordinated motion. This includes traditional cell decomposition algorithms and algorithms combined with machine learning to improve path planning efficiency. Finally, the above methods were summarized, and the impact of other possible feasible methods in the field of artificial intelligence was explored and analyzed.

A self-organizing robotic aggregate using solid and liquid-like collective states.

Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units that each contain a single actuator such that units can self-assemble into larger, granular aggregates using magnetic coupling. These aggregates can reconfigure dynamically and also split into subsystems that might later recombine. Aggregates can self-organize into collective states with solid- and liquid-like properties, thus displaying widely differing compliance. These states can be perturbed locally via actuators or externally via mechanical feedback from the environment to produce adaptive shape-shifting in a decentralized manner. This, in turn, can generate locomotion strategies adapted to different conditions. Aggregates can move over obstacles without using external sensors or coordinates to maintain a steady gait over different surfaces without electronic communication among units. The modular design highlights a physical, morphological form of control that advances the development of resilient robotic systems with the ability to morph and adapt to different functions and conditions.

Robot swarms meet soft matter physics.

Principles of soft matter physics can be leveraged to develop swarms of active robots with unique properties.

Steps towards Satisficing Distributed Dynamic Team Trust

Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains. Team members should be trusted to work towards agreed goals and in accordance with shared values. In this paper, our concern is with the definition of goals and values such that it is possible to define ‘trust’ in a way that is interpretable, and hence usable, by both humans and robots. We argue that the outcome of team activity can be considered in terms of ‘goal’, 'individual/team values', and ‘legal principles’. We question whether alignment is possible at the level of 'individual/team values', or only at the 'goal' and ‘legal principles’ levels. We argue for a set of metrics to define trust in human-robot teams that are interpretable by human or robot team members, and consider an experiment that could demonstrate the notion of 'satisficing trust' over the course of a simulated mission.

The eXchange Calculus (XC): A functional programming language design for distributed collective systems

Distributed collective systems are systems formed by homogeneous dynamic collections of devices acting in a shared environment to pursue a joint task or goal. Typical applications emerge in the context of wireless sensor networks, robot swarms, groups of wearable-augmented people, and computing infrastructures. Programming such systems is notoriously hard, due to requirements of scalability, concurrency, faults, and difficulty in making desired collective behaviour ultimately emerge: ad-hoc languages and mechanisms have been proposed threads like spatial computing, macro-programming, and field-based coordination.In this paper we present the eXchange Calculus (XC), formalising a tiny set of key mechanisms, usable across many different languages and platforms, allowing to express the overall interactive behaviour of distributed collective systems in a declarative way. In this approach, computation (executed in asynchronous rounds), communication (which is neighbour-based), and state over time, are all expressed by a single declarative construct, called exchange. We provide a formalisation of XC in terms of syntax, device-level and network-level semantics, prove a number of properties of the calculus, and discuss applicability considering a smart city scenario. XC is implemented as a DSL in Scala and in C＋＋, with different trade-offs in terms of productivity and platform targetting.

On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots

On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots

A communication-based identification of critical drones in malicious drone swarm networks

Accurate identification of critical malicious drones is crucial for optimizing directed energy attacks and maximizing their effectiveness. However, current studies on critical drone identification are still in the preliminary stage and almost rely on the traditional centrality methods that do not address the distributed features of drone swarms. This leads to inaccurate identification of critical drones, resulting in the low efficiency of directed energy attacks. Therefore, this paper proposes a new critical drone identification method based on the distributed features, communication intensity, and communication scale of drones. Specifically, this paper first constructs a dynamic communication prediction network (DCPN) of drone swarms based on the 3D position and interaction range, which predicts the dynamic communication between drones. Then, this paper proposes a new method called dynamic giant connected component (GCC)-based scale-intensity centrality (DGSIC) that combines the local, global, and community structure of DCPN to identify critical nodes with stronger communication capabilities. The dynamic strategy involves the iterative identification of one critical node at each step, considering the evolving network configuration and ensuring the identified node remains the most critical in the present network. Additionally, the prioritization strategy is employed to identify the nodes within the GCC, which can significantly impact the network connectivity and communication. DGSIC optimizes the attack sequence for directed energy attacks, facilitating the rapid dissolution of malicious drone swarms. Extensive experiments in four simulated networks and eight real-world networks demonstrate the superior robustness and cascading failure performance of DGSIC.

Self-organized swarm robot for multi-target trapping based on self-regulated density interaction

In swarm robotics, multi-target trapping usually relies on global information or explicit communication, posing a challenge for robots to autonomously self-organize and trap multiple targets with only local perceptual data. We present a self-regulated density-based approach for self-organized multi-target trapping. This method employs density-based negative feedback control and density-distance weighted target selection. Our approach leverages distributed perception, where robots perceive nearby peers within their sensory range, eliminating the need for explicit information exchange and enabling autonomous trapping. During task execution, negative feedback control adjusts swarm density, ensuring it reaches an optimal level. Building on this, individual robots use density fields and relative target positions to select suitable targets, achieving self-regulated dispersed selection and multi-target trapping. We validate our approach through numerical simulations and real-world robot experiments. Results demonstrate stable self-organization, efficient self-regulated dispersed target selection, and successful target trapping, even with a limited number of trapping robots in low-redundancy scenarios.

Unmanned vehicles on the rise: a review on projects of cooperating robot teams

Abstract Teams consisting of cooperating mobile robots are able to carry out tasks faster and more effectively than individual robots. Previous research projects have focused on teams of homogeneous robots in particular, whereas heterogeneous robots are becoming increasingly important. In this paper, the fundamentals of both homogeneous and heterogeneous robot teams are examined. In this context, not only key fundamentals are distinguished from each other, but also potential applications and advantages of multi-robot systems (MRS) are shown. The rapid development of autonomous MRS is shown by means of exemplarily selected research projects, whereby the individual approaches of these projects are systematically compared and evaluated. Finally, an outlook on remaining challenges in the field of autonomous heterogeneous MRS is given.

Heterogeneous Robot Teams with Unified Perception and Autonomy: How Team CSIRO Data61 Tied for the Top Score at the DARPA Subterranean Challenge

Heterogeneous Robot Teams with Unified Perception and Autonomy: How Team CSIRO Data61 Tied for the Top Score at the DARPA Subterranean Challenge

Action Recognition for Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities

Human–robot teaming (HrT) is being adopted in an increasing range of industries and work environments. Effective HrT relies on the success of complex and dynamic human–robot interaction. Although it may be optimal for robots to possess all the social and emotional skills to function as productive team members, certain cognitive capabilities can enable them to develop attitude-based competencies for optimizing teams. Despite the extensive research into the human–human team structure, the domain of HrT research remains relatively limited. In this sense, incorporating established human–human teaming (HhT) elements may prove practical. One key element is mutual performance monitoring (MPM), which involves the reciprocal observation and active anticipation of team members’ actions within the team setting, fostering enhanced team coordination and communication. By adopting this concept, this study uses ML-based visual action recognition as a potential tool for developing an effective way to monitor the human component in HrT. This study utilizes a data modeling approach on an existing dataset, the “Industrial Human Action Recognition Dataset” (InHARD), curated specifically for human action recognition assembly tasks in industrial environments involving human–robot collaborations. This paper presents the results of this modeling approach in analyzing the dataset to implement a theoretical concept that can be a first step toward enabling the system to adapt dynamically. The outcomes emphasize the significance of implementing state-of-the-art team concepts by integrating modern technologies and assessing the possibility of advancing HrT in this direction.