Team Of Robots Research Articles

Robust Optimization Models for Planning Drone Swarm Missions

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks.

Emergence of social phases in human movement.

Recent empirical studies have found different thermodynamic phases for collective motion in animals. However, such a thermodynamic description of human movement remains unclear. Existing studies of traffic and pedestrian flows have primarily focused on relatively high-speed mobility data, revealing only a fluidlike phase. This focus is partly because the parameter space of low-speed movement, which is governed predominantly by pairwise social interaction, remains largely uncharted. Here, we used ultrawideband radio frequency identification (UWB-RFID) technology to collect high-resolution spatiotemporal data on movements in four different classroom and playground settings. We observed two unique social phases in children's movements: a gaslike phase of free movement and a liquid-vapor coexistence phase characterized by the formation of small social groups. We also developed a simple statistical physics model that can reproduce different empirically observed phases. The proposed UWB-RFID technology can also be used to study the dynamics of active matter systems, including animal behavior, coordinating robotic swarms, and monitoring human interactions within complex systems, potentially benefiting future research in social physics.

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

The use of digital twin for mobile robot swarm task allocation

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

Multi-agent long-distance end-to-end indoor navigation: using imitation learning pre-training and global map

Abstract To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

Automated Discovery of Efficient Behavior Strategies for Distributed Shape Formation of Swarm Robots by Genetic Programming

Automated Discovery of Efficient Behavior Strategies for Distributed Shape Formation of Swarm Robots by Genetic Programming

Energy Efficiency Deep Reinforcement Learning for URLLC in 5G Mission-Critical Swarm Robotics

Energy Efficiency Deep Reinforcement Learning for URLLC in 5G Mission-Critical Swarm Robotics

Bridging the reality gap in drone swarm development through mixed reality

AbstractSwarm algorithms promise to solve certain problems in large multi-robot systems. The evaluation of large swarms is however challenging as simulations alone often lack some properties of real systems whereas real-world experiments are costly and complex. We present a mixed reality (MR) system that connects simulated and physical robots though a 5G network, facilitating MR experiments to evaluate communication-based swarm algorithms. The effectiveness of the system is demonstrated through extensive experiments with unmanned aerial vehicles. Measurements show that the communication requirements of swarm coordination are well met by 5G but the computing power of the simulation server can be a bottleneck. However, even when the simulation slows down, communication and coordination take place in real time. In conclusion, 5G-enabled MR experiments are a feasible tool for bridging the reality gap in the development and evaluation of robot swarms.

TIP: A trust inference and propagation model in multi-human multi-robot teams

Trust is a crucial 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 and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=30$$\\end{document}). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.

Explainable machine learning model of disorganisation in swarms of drones.

The main challenges when managing a fleet of unmanned aerial vehicles are to ensure the relative stability of its formation and to minimise disorganisation, specifically when undergoing an intrusion. When planning the mission it is beneficial for the operator to set the parameters of the formation to balance the needs of the mission with the disorganisation that an intruder may cause. The model developed in this research predicts the anticipated disturbance as a function of the parameters of the formation. The effectiveness of six machine learning methods are compared with a previously established baseline, using data obtained from simulations. CatBoost (categorical boosting) delivered the best results, with an (coefficient of determination) value of 83.3%, representing an improvement of 80% over the baseline. The SHAP (Shapley Additive Explanations) method was used to extend the model beyond predictability for particular combinations of values of parameters, towards generalised recommendations for the operator of the formation.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

Drone Swarm Classification from ISAR Imaging

In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.

Проблема ценности в цифровом обществе роевого интеллекта

Introduction. The emerging digital society demonstrates the problem of justifying the value, worth and price of digital products and services, which requires consideration of the specificity of value in this society. Theoretical analysis. Following the financial and art market, the boundary between “value” and “worth” is blurred in the digital society. Value, including the value of an individual, becomes variable and is conditioned by the presence of unique attributes and unique experiences that obtain market value. At the same time, under standard digital protocols and technologies, the true uniqueness of an individual experience can only be achieved through the creation of customized digital ecosystems in the form of robotic swarms that support human life activities. A set of digital devices that have sufficient resources to continuously self-renew and form a closed environment around the individual that provides a unique experience can be called a chrysalic swarm. The chrysalic swarm creates an opportunity for the individual to escape from the logic of mutual competition dictated by society. Conclusion. Studying the value problematization in the digital society of swarm intelligence opens a theoretical perspective on the problem of the crisis of social elites in the context of the fulfillment of this scenario of society development.

Exploring advancements and emerging trends in robotic swarm coordination and control of swarm flying robots: A review

Swarm Robotics (SR) is an interdisciplinary field that is rapidly advancing to address complex industrial challenges. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of Swarm Flying Robots (SFRs). The motivation behind this review is to explore scalable and robust solutions for SFRs to enhance their performance and adaptability across various applications. Key objectives include examining the characteristics and essential behaviors of SR, analyzing the challenges and so lutions for implementing SR in Flying Robots (FRs), and highlighting current and future research directions. The review delves into critical areas such as multiple robot path planning, Swarm Intelligence (SI), combinatorial optimization, and formation flying using SFR. Special attention is given to coordination and control techniques, including formation control in GPS-denied environments, to underscore their significance in advancing SR. The paper also addresses ethical, privacy, and security considerations, emphasizing the importance of responsible practices in SR development. Major takeaways from this review include the identification of key technical challenges and potential solutions in SFR, the exploration of SI algorithms, and the future research directions necessary for fully realizing the potential of SR technologies. By offering detailed insights into state-of-the-art research and its industrial implications, this paper serves as a foundational guide for future studies in the dynamic and promising domain of swarm robotics.

Arbitrary pattern formation on a continuous circle by oblivious robot swarm

In the field of distributed systems, the Arbitrary Pattern Formation (APF) problem is an extensively studied problem. The purpose of APF is to design an algorithm to move a swarm of robots to a particular position in an environment (discrete or continuous) such that the swarm can form a specific but arbitrary pattern given previously to every robot as an input. In this paper, the solvability of the APF problem on a continuous circle is discussed for a swarm of oblivious and silent robots without chirality under a semi-synchronous scheduler. Firstly a class of configurations (the initial placements of the robots on the circle) called Formable Configuration (FC) has been provided which is necessary to solve the APF problem on a continuous circle. Then considering the initial configuration to be an FC, a deterministic and distributed algorithm has been provided that solves the APF problem for n robots on a continuous circle of fixed radius within O(n) epochs without collision, where an epoch is considered to be a time interval in which all robots are activated at least once.

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

A novel solution for routing a swarm of drones operated on a mobile host

The increasing use of drones across various sectors demands optimized deployment strategies under diverse constraints. This paper tackles the Multiple Capacitated Mobile Depot Vehicles Routing Problem (mCMoD-VRP), a challenging variant of the Vehicle Routing Problem (VRP) where multiple drones with limited flight range operate from a mobile depot. The goal is to maximize target coverage while considering flight endurance, depot mobility, and drone multiplicity. We introduce a novel evolutionary algorithm, Evolutionary Optimization for Synchronized Routing Problem (EOSRP), which constructs synchronized routes for the drone swarm, accounting for all constraints. EOSRP distinguishes itself with specialized genetic operators, specifically designed to efficiently handle the constraints of mCMoD-VRP, enhancing both exploration and exploitation of the search space. EOSRP also facilitates collaborative planning among drones, enabling them to share targets and optimize routes collectively, resulting in more efficient use of flight range capacity. Comprehensive simulations on benchmark problems demonstrate that EOSRP consistently outperforms a serialized version of our previous single-drone algorithm, Genetic Algorithm for Capacitated Mobile Depot (GA-CMoD), achieving an average of 8.7% higher target coverage and 7.28% more efficient use of flight range capacity. EOSRP’s ability to generate synchronized solutions through collaborative planning leads to significantly improved mission efficiency.

Sociotechnical considerations on developing human robot teaming solutions for construction: a case study

This research advocates for a paradigm shift in the exploration of human–robot teaming solutions for construction automation, by focusing on an integrated view of sociotechnical systems (STS) that recognize the inter-dependencies among actors at various levels when tracing how innovative ideas about intelligent robotic technologies translate into practice in the construction sector. Through a qualitative case study, the paper examines industry and organizational considerations for developing and adopting robotic technologies, leadership vision, mediation, and change management to propose integrative strategies to enhance expectations, acceptance, and deployment of intelligent technologies in human–robot teams (HRTs). This study contributes to research in construction robotics at three organizational levels—macro, meso, and micro. The Integrated Human–Robot Teaming Framework and associated workplan schema offer guidance for navigating human–robot teaming complexities. The study recommends adopting STS principles in planning and deploying robotics applications for construction, emphasizing the integration of multiple elements across the lifecycle. Active leadership and mediation emerge as critical elements in navigating complex networks, ensuring successful outcomes in the dynamic construction environment.

Tracking the trajectory of a swarm of mobile robots with a computer vision system

Swarm robotics research uses a range of tools for evaluating the behaviors and metrics of robot collectives. One crucial tool involves the capability to track each robot’s position and orientation at various intervals, enabling the reconstruction of individual robot poses and trajectories. Comprehensive analysis of swarm behavior hinges on the study of the collective trajectories of each robot within the group. This paper demonstrates the implementation of a computer vision system, utilizing a webcam and Python scripts, to effectively track a mobile robot group within a swarm. This shows the feasibility of developing such research tools using commonplace computing equipment. The design and development of the vision system, including a detailed calibration procedure, robot identification methods, and practical examples, are also shown. Furthermore, it offers an exhaustive explanation of the robot tracking process. Experimental trials with three robots validate the system’s ability to extract images from video feeds and accurately identify each robot. Subsequently, after image processing, the system generates a dataset encompassing image numbers, robot IDs, x and y positions, and orientations.