Swarm Interaction Research Articles

Swarm manipulation: An efficient and accurate technique for multi-object manipulation in virtual reality

The theory of swarm control shows promise for controlling multiple objects, however, scalability is hindered by cost constraints, such as hardware and infrastructure. Virtual Reality (VR) can overcome these limitations, but research on swarm interaction in VR is limited. This paper introduces a novel swarm manipulation technique and compares it with two baseline techniques: Virtual Hand and Controller (ray-casting). We evaluated these techniques in a user study (N = 12) in three tasks (selection, rotation, and resizing) across five conditions. Our results indicate that swarm manipulation yielded superior performance, with significantly faster speeds in most conditions across the three tasks. It notably reduced resizing size deviations but introduced a trade-off between speed and accuracy in the rotation task. Additionally, we conducted a follow-up user study (N = 6) using swarm manipulation in two complex VR scenarios and obtained insights through semi-structured interviews, shedding light on optimized swarm control mechanisms and perceptual changes induced by this interaction paradigm. These results demonstrate the potential of the swarm manipulation technique to enhance the usability and user experience in VR compared to conventional manipulation techniques. In future studies, we aim to understand and improve swarm interaction via internal swarm particle cooperation.

Extracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals

Extracting Human Levels of Trust in Human–Swarm Interaction Using EEG Signals

Human–Swarm Interaction Simulation Platform to Assess Sense of Agency

Human–Swarm Interaction Simulation Platform to Assess Sense of Agency

Integrated system architecture with mixed-reality user interface for virtual-physical hybrid swarm simulations

This paper introduces a hybrid robotic swarm system architecture that combines virtual and physical components and enables human–swarm interaction through mixed reality (MR) devices. The system comprises three main modules: (1) the virtual module, which simulates robotic agents, (2) the physical module, consisting of real robotic agents, and (3) the user interface (UI) module. To facilitate communication between the modules, the UI module connects with the virtual module using Photon Network and with the physical module through the Robot Operating System (ROS) bridge. Additionally, the virtual and physical modules communicate via the ROS bridge. The virtual and physical agents form a hybrid swarm by integrating these three modules. The human–swarm interface based on MR technology enables one or multiple human users to interact with the swarm in various ways. Users can create and assign tasks, monitor real-time swarm status and activities, or control and interact with specific robotic agents. To validate the system-level integration and embedded swarm functions, two experimental demonstrations were conducted: (a) two users playing planner and observer roles, assigning five tasks for the swarm to allocate the tasks autonomously and execute them, and (b) a single user interacting with the hybrid swarm consisting of two physical agents and 170 virtual agents by creating and assigning a task list and then controlling one of the physical robots to complete a target identification mission.

Autonomous UAV Swarms: Powering Up with AI

Abstract: This study offers a thorough analysis of autonomous UAV swarming, focusing on its merits, important technologies, applications, difficulties, and prospects for the future. The idea of autonomous UAV swarming entails the coordination and decentralised cooperation of several UAVs to accomplish common objectives. The foundations of UAV swarming are thoroughly covered, including its essential ideas, advantages, architectures, and swarm behaviour. We emphasise the role of artificial intelligence (AI), more especially reinforcement learning (RL) and particle swarm optimisation (PSO), in promoting thoughtful decision-making, task allocation, and coordination within UAV swarms. The study examines the many uses of autonomous UAV swarming in fields including infrastructure inspection, search and rescue, agriculture, and disaster response. It also discusses UAV swarming's difficulties and restrictions, such as scalability, communication, fault tolerance, and ethical issues. In addition to standardisation initiatives, the study suggests future research possibilities for swarm intelligence, edge computing, cognitive capacities, and human swarm interaction.

Human-robot swarm interaction: coordinated role of human mind mindsets and robot group entitativity

ABSTRACT The one-on-one human–robot interaction has expanded to the group level; robot groups are increasingly exerting psychosocial implications on human beings. However, how people interact with robot groups, especially how human factors and robot group factors coordinate to influence people’s responses to robot groups, is underexplored. To investigate this issue, the present study examined the interaction effect between individual differences in fixed and growth mindsets about the human mind and the fundamental characteristics of robot groups (i.e. entitativity) on responses to the robots during human–robot interaction. We induced mindsets (fixed or growth) about the human mind and manipulated the level of robot group entitativity (high or low) to capture responses to robots during human–robot interaction using virtual reality (VR) technology. The results revealed that a growth (versus fixed) mindset about the human mind promoted self-disclosure toward, and reduced behavioural anxiety with respect to robot groups with high (versus low) entitativity. We found that increased psychological closeness with robots accounted for these effects. Our findings contribute to research on the determinants of human–robot relationships and present implications for human–robot interactions at the group level.

Analysis of UAV Thermal Soaring via Hawk-Inspired Swarm Interaction.

A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm's performance is typically limited by each agent's stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm's distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs' motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms.

Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

We conducted a Systematic Literature Review on scientific papers that examined the interaction between operators and drone swarms based on the use of a command and control center. We present the results of a meta-analysis of nine scientific papers published in the ACM DL and IEEE Xplore databases. Our findings show that research on human-drone swarm interaction shows a disproportionate interest in hand gestures compared to other input modalities for drone swarm control. Furthermore, all articles reviewed exclusively explored gestures and the size of the swarm used in the studies was limited, with a median of 3.0 and an average of 3.8 drones per study. We compiled an inventory of interaction modalities, recognition techniques, and application types from the scientific literature, which is presented in this paper. On the basis of our findings, we propose four areas for future research that can guide scientific investigations and practical developments in this field.

Augmented reality for human–swarm interaction in a swarm-robotic chemistry simulation

We present a novel augmented reality (AR) framework to show relevant information about swarm dynamics to a user in the absence of markers by using blinking frequency to distinguish between groups in the swarm. In order to distinguish between groups, clusters of the same group are identified by blinking at a specific time interval that is distinct from the time interval at which their neighbors blink. The problem is thus to find blinking sequences that are distinct for each group with respect to the group’s neighbors. Selecting an appropriate sequence is an instance of the distributed graph coloring problem, which can be solved in $$O(\log (n))$$ time with n being the number of robots involved. We demonstrate our approach using a swarm chemistry simulation in which robots simulate individual atoms that form molecules following the rules of chemistry. An AR display is then used to display information about the internal state of individual swarm members as well as their topological relationship, corresponding to molecular bonds in a context that uses robot swarms to teach basic chemistry concepts.

ПОРІВНЯЛЬНИЙ АНАЛІЗ ТА СТВОРЕННЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ ДЛЯ ФОРМУВАННЯ МАКЕТА РОЙОВОЇ ВЗАЄМОДІЇ

The subject of study is the process of creating an unmanned aerial vehicle of the helicopter type, which is able to perform autonomous flight and be used in the premises to test the algorithms of swarm interaction. The aim is to reduce the cost of forming and operating the UAV swarm intelligence model, ensuring compatibility with existing hardware positioning systems and simulation software platforms. Tasks: to analyze the existing prototypes of unmanned aerial vehicles of the helicopter type, used for modeling and testing of autonomous flight modes indoors; to review the systems of positioning and exchange of information between the participants of the swarm formation and to analyze the hardware systems necessary for the implementation of these systems; to develop the architecture of hardware subsystems for unmanned aerial vehicles capable of performing tasks at the level of the considered prototypes; design a case for a sound hardware platform, check its operational parameters, implement its collection and testing; install software that allows you to check the operability of the considered platform of the unmanned aerial vehicle. The methods used are: systems analysis used to compare models of unmanned aerial vehicles within the tasks, methods of analysis and synthesis of schemes for the implementation of the hardware platform, the method of graphic modeling for the design of the unmanned aerial vehicle, methods of system programming for flight program. The following results were obtained. The choice of the hardware platform of the unmanned aerial vehicle is substantiated; the hull for the helicopter was developed and implemented, two platforms of unmanned aerial vehicles were collected and tested during autonomous flight indoors. Conclusions. The study found that there are existing and widely used platforms for compatible indoor flight. These platforms are open and closed. The analysis of open hardware platforms and synthesis of architecture of own unmanned aerial vehicle is made. The designed and implemented unmanned aerial vehicle is capable of autonomous flight, has all the necessary components to perform a compatible flight program. As part of the layout of swarm interaction, it is necessary to further adjust the positioning system.

Designing a User-Centered Interaction Interface for Human–Swarm Teaming

A key challenge in human–swarm interaction is to design a usable interface that allows the human operators to monitor and control a scalable swarm. In our study, we restrict the interactions to only one-to-one communications in local neighborhoods between UAV-UAV and operator-UAV. This type of proximal interactions will decrease the cognitive complexity of the human–swarm interaction to O(1). In this paper, a user study with 100 participants provides evidence that visualizing a swarm as a heat map is more effective in addressing usability and acceptance in human–swarm interaction. We designed an interactive interface based on the users’ preference and proposed a controlling mechanism that allows a human operator to control a large swarm of UAVs. We evaluated the proposed interaction interface with a complementary user study. Our testbed and results establish a benchmark to study human–swarm interaction where a scalable swarm can be managed by a single operator.

Kin discrimination drives territorial exclusion during Bacillus subtilis swarming and restrains exploitation of surfactin

Swarming is the collective movement of bacteria across a surface. It requires the production of surfactants (public goods) to overcome surface tension and provides an excellent model to investigate bacterial cooperation. Previously, we correlated swarm interaction phenotypes with kin discrimination between B. subtilis soil isolates, by showing that less related strains form boundaries between swarms and highly related strains merge. However, how kin discrimination affects cooperation and territoriality in swarming bacteria remains little explored. Here we show that the pattern of surface colonization by swarming mixtures is influenced by kin types. Closely related strain mixtures colonize the surface in a mixed swarm, while mixtures of less related strains show competitive exclusion as only one strain colonizes the surface. The outcome of nonkin swarm expansion depends on the initial ratio of the competing strains, indicating positive frequency-dependent competition. We find that addition of surfactin (a public good excreted from cells) can complement the swarming defect of nonkin mutants, whereas close encounters in nonkin mixtures lead to territorial exclusion, which limits the exploitation of surfactin by nonkin nonproducers. The work suggests that kin discrimination driven competitive territorial exclusion may be an important determinant for the success of cooperative surface colonization.

The design of self-organizing human–swarm intelligence

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

Swarm Robotic Interactions in an Open and Cluttered Environment: A Survey

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages.

Autonomous recommender system for reconnaissance tasks using a swarm of UAVs and asynchronous shepherding

Several artificial intelligence-based systems have been proposed to help human operators in safety-critical domains such as air-traffic control systems, health and farming. In scenarios where multiple autonomous systems are utilised, the cognitive capacity of humans hits its limits quickly, and sometimes the success of these systems is obstructed. Designing systems to sustain human performance over longer periods through balancing the load between human operators and autonomous systems can reduce errors and improve overall performance. Future human-autonomy systems need to be designed in a way that makes them scalable in terms of capabilities and safety, yet flexible enough to adapt to the cognitive workload of human operators. In this paper, we propose an adaptive human swarm interaction system for maintaining human operator performance in supervisory-control aerial-surveillance tasks. A bio-inspired guidance and control shepherding agent is proposed to support the human operator to guide multiple assets simultaneously using a single point of control. The proposed system employs an adjustable autonomous recommender system to provide the human operator with timely recommendations in real-time sittings. The proposed system is designed in a flexible way that allows it to adapt its rate of providing these recommendations in response to the needs of human operators. Simulation-based experiments are conducted to validate and evaluate the proposed recommender system. Experimental results demonstrate the effectiveness and efficiency of the proposed recommender system in minimising control errors while maintaining low computational costs that allow it to be used in a real-time applications.

Uncovering the social interaction network in swarm intelligence algorithms

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.

Immersive Interactive Virtual Fish Swarm Simulation Based on Infrared Sensors

Virtual simulation and 3D interaction have shown great potentials in a variety of domains for our future life. For a virtual fish swarm simulation system, the simulation of cohesion behaviors of fish swarm and the interaction between human and fish swarm are two key components to create immersive interactive experiences. However, it is a huge challenge to create a realistic fish swarm simulation system while providing a natural and comfortable interaction. In this paper, we propose a method for immersive virtual fish swarm simulation based on infrared sensors. Based on dynamic weight constraints, we propose a particle swarm optimization method for fish swarm cohesion simulation, which separates a particle swarm by the state of each particle and dynamically controls the particle swarm, making the movement behavior of virtual fish more realistic. In addition, an interactive fast skinning method is proposed for cartoon fishes, which leverages image segmentation, Optical Character Recognition (OCR) and bone skinning are used to generate cartoon fishes based on user-created colors. With infrared sensors, we propose a method for virtual fish swarm interaction, where the positions of human skeleton are processed by an action analyzer, achieving real-time user interactions with fish swarms. With all the proposed techniques integrated in a system, the experimental results show that our method is feasible and effective.

Ahoi

The recent development of small, cheap AUVs enables a plethora of underwater near- and inshore applications. Among these are monitoring of wind parks, detection of pollution sources, water-quality inspection, and the support of divers during disaster management. These tasks profit from online reporting, control, and AUV swarm interaction; yet they require underwater communication. Unfortunately, commercial devices are prohibitively expensive and typically closed-source, hampering their application in affordable products and research. Therefore, we developed the open-source ahoi acoustic modem. It is (i) small enough to be carried by micro AUVs, (ii) consumes little enough energy to not diminish operation times of its host, (iii) comes at an attractive unit cost below $600, (iv) can reliably communicate at distances of 150 m and more, and (v) supports ranging without additional hardware. Due to its modular build, the modem can be customized and is suitable as research platform to analyze, e.g., MAC and routing protocols. We conducted extensive real-world studies and present results of communication range, packet reception rate, ranging accuracy, and efficient and reliable self-localization. Finally, we draw conclusions regarding acoustic communication, ranging, and localization with inexpensive and low-power devices that go beyond a particular device. Our study, hence, encompasses general insights, observations, and recommendations.

A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

In this article, we present a novel high-performance millirobot ( milli- robot- Toronto), designed to allow for the testing of complex swarm-behaviours, including human–swarm interaction. milli- robot- Toronto, built only with off-the-shelf components, has locomotion, processing and sensing capabilities that significantly improve upon existing designs, while maintaining one of the smallest footprints among current millirobots. As complementary software to this hardware development, herein, we also present a new global swarm-topology estimation algorithm. The method is novel in that it uniquely fuses incomplete location data collected by the individual robots in a distributed manner to optimally estimate the topology of the overall swarm using a centralized computer. It is a generalized technique usable by any swarm comprising robots capable of collecting location estimates of neighbouring robots. Numerous experiments, evaluating the performance of milli- robot- Toronto and the proposed optimal swarm-topology estimation algorithm, are also included.

Distribution of Roles in Groups of Robots with Limited Communications Based on the Swarm Interaction

The paper is aimed at the problem of the distribution of roles in groups of robots with limited communications and based on the principles of the swarm intelligence. The iteration approach to the distribution of roles in a group of robots is proposed. There are results of the study and computer simulation of the proposed approach.