Team Of Robots Research Articles

Enhancing Robustness of Swarm Robotics Systems in a Perceptual Discrimination Task

Enhancing Robustness of Swarm Robotics Systems in a Perceptual Discrimination Task

Swarm Robotics Navigation Task: A Comparative Study of Reinforcement Learning and Particle Swarm Optimization Methodologies

Automatic design methods focus on generating the collective behavior of swarm robotic systems. These methods enable multiple robots to coordinate and execute complex tasks in their environments autonomously. This research paper investigated two prominent methodologies: particle swarm optimization (PSO) and reinforcement learning (RL). A new comparative study was conducted to analyze the performance of a group of mobile robots through extensive experimentation. The objective was to produce navigational collective behavior through unknown environments. These environments differ in complexity ranging from obstacle-free environments to cluttered ones. The core metrics of the comparison include the time efficiency of individual robots and the overall swarm, flexibility in pathfinding, and the ability to generalize solutions for new environments. The obtained results from the Webots simulator with Python controller suggested that RL excels in environments closely aligned with its training conditions. RL achieved a faster completion time and demonstrated superior coordination among individual robots. However, its performance dips when facing untrained scenarios necessitating computationally expensive retraining or structural complexities to enhance adaptability. Conversely, PSO showed commendable consistency in performance. Despite its slower pace, it exhibited robustness in various challenging settings without reconfiguration.

Multi-objective QoS optimization in swarm robotics

The “Internet of Robotic Things” (IoRT) is a concept that connects sensors and robotic objects. One of the practical applications of IoRT is swarm robotics, where multiple robots collaborate in a shared workspace to accomplish assigned tasks that may be challenging or impossible for a single robot to conquer. Swarm robots are particularly useful in critical situations, such as post-earthquake scenarios, where they can locate survivors and provide assistance in areas inaccessible to humans. In these life-saving situations, reliable and prompt communication among swarm robots is of utmost importance. To address the need for highly dependable and low-latency communication in swarm robotics, this research introduces a novel hybrid approach called Multi-objective QoS optimization based on Support vector regression and Genetic algorithm (MQSG). The MQSG method consists of two main phases: Parameter Relationship Identification and Parameter Optimization. In the Parameter Relationship Identification phase, the relationship between network inputs (Packet inter-arrival time, Packet size, Transmission power, Distance between sender and receiver) and outputs (quality of service (QoS) parameters) is established using support vector regression. In the parameter optimization phase, a multi-objective function is created based on the obtained relationships from the Parameter Relationship Identification phase. By solving this multi-objective function, optimal values for each QoS parameter are determined, leading to enhanced network performance. Simulation results demonstrate that the MQSG method outperforms other similar algorithms in terms of transmission latency, packet delivery rate, and the number of retransmitted packets.

Consensus guidelines on the bedside assistant skills required in robotic surgery

BackgroundWhile bedside assistants play a critical role in many robotic operations, substantial heterogeneity remains in bedside assistant training pathways. As such, this study aimed to develop consensus guidelines for bedside assistant skills required for team members in robotic operations.MethodsWe designed a study using the Delphi process to develop consensus guidelines around bedside assistant skills. We generated an initial list of bedside assistant skills from the literature, training materials, and expert input. We selected experts for the Delphi process based on prior scholarship in the area of robotic bedside assistant education and experience facilitating robotic bedside assistant training. For each item, respondents specified which robotic team members should have the skill from a list of “basic” bedside assistants, “advanced” bedside assistants, surgeons, surgical technologists, and circulating nurses. We conducted two rounds of the Delphi process and defined 80% agreement as sufficient for consensus.ResultsFourteen experts participated in two rounds of the Delphi process. By the end of the second round, the group had reached consensus on 253 of 305 items (83%). The group determined that “basic” bedside assistants should have 52 skills and that “advanced” bedside assistants should have 60 skills. The group also determined that surgeons should have 54 skills, surgical technologists should have 25 skills, and circulating nurses should have 17 skills. Experts agreed that all participants should have certain communication skills and basic knowledge of aspects of the robotic system.ConclusionsWe developed consensus guidelines on the skills required during robotic surgery by bedside assistants and other team members using the Delphi process. These findings can be used to design training around bedside assistant skills and assess team members to ensure that each team member has the appropriate skills. Hospitals can also use these guidelines to standardize expectations for robotic team members.

Drone Swarm Robust Cooperative Formation Pursuit through Relative Positioning in a Location Denial Environment

This paper considers the pursuit problem of a moving target by a swarm of drones through a flexible-configuration formation. The drones are modeled by second-order systems subject to uncertain damping ratios, whereas the moving target follows a polynomial-type trajectory whose coefficient vectors are fully unknown. Due to location denial, drones cannot obtain their absolute positions, but they can obtain their positions relative to other neighboring drones and the target. To achieve a robust formation pursuit, a robust cooperative control protocol is synthesized, which comprises three key components, namely, the pseudo drone position estimator, the pseudo target position estimator, and the local internal model control (IMC) law. The pseudo drone position estimator and the pseudo target position estimator aim to recover for each drone the position of itself and the target, respectively, but are subject to some common unknown constant bias in a distributed manner. By subtracting the pseudo target position from the pseudo drone position, each drone can acquire its position relative to the target, which facilitates the design of a local IMC law to fulfill formation pursuit in the presence of system parametric uncertainties. Both pure numerical simulation and hardware-in-the-loop (HIL) simulation are performed to verify the effectiveness of the proposed control protocol.

Bilayer Real Time Multi-Robot Communication Maintenance Deployment Framework for Robot Swarms

Abstract The communication maintenance problem of robot swarms is important to multi-robot control in applications like rescue and area exploration. In this paper, we propose a robot-relay-based framework to keep the robot swarm connected from the view of Line-of-Sight communication. This framework mainly consists of a graphic calculation layer and a numerical optimization layer. The combination of the two layers is intent on blending the combinatorial identity of the problem and the advantage of the differentiable nature of this numerical optimization problem and forming a pipeline for generating a swarm deployment solution with low communication loss, low energy consumption, and high efficiency.

Extending boids for safety-critical search and rescue

Robot swarms can accomplish complex tasks, and in this work, we seek to design swarm robotic algorithms for search and rescue that are scalable to large swarms, efficient in terms of computations, safe from collisions, and tunable to mediate the trade-off between exploration and exploitation in the search. We propose extending the Boids algorithm to accomplish this. Without modifying the three Boids rules of alignment, cohesion, and separation, we add target-seeking and general collision avoidance by using ghost boids. Additionally, we use a control barrier function to improve safety at the cost of increased computation. Via simulation in a search and rescue task, we analyze the trade-offs between safety, computational efficiency, and coverage of the environment for our algorithm.

Stigmergy: from mathematical modelling to control.

Stigmergy, the indirect communication between agents of a swarm through dynamic environmental modifications, is a fundamental self-organization mechanism of animal swarms. Engineers have drawn inspiration from stigmergy to establish strategies for the coordination of swarms of robots and of mixed societies of robots and animals. Currently, all models of stigmergy are algorithmic, in the form of behavioural rules implemented at an individual level. A critical challenge for the understanding of stigmergic behaviour and translation of stigmergy to engineering is the lack of a holistic approach to determine which modifications of the environment are necessary to achieve desired behaviours for the swarm. Here, we propose a mathematical framework that rigorously describes the relationship between environmental modifications and swarm behaviour. Building on recent strides in continuification techniques, we model the swarm and environmental modifications as continua. This approach allows us to design the environmental modifications required for the swarm to behave as desired. Through analytical derivations and numerical simulations of one- and two-dimensional examples, we show that our framework yields the distribution of traces required to achieve a desired formation. Such an approach provides an adaptable framework for different implementation platforms, from robotic swarms to mixed societies of robots and animals.

A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector

Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.

Software Synthesis From High-Level Specification for Swarm Robotic Applications

Software Synthesis From High-Level Specification for Swarm Robotic Applications

Human Empowerṇṇment as a Key Component of Human-AI Robot Teaming

Human Empowerṇṇment as a Key Component of Human-AI Robot Teaming

Enhancing radioactive environment exploration with bio-inspired swarm robotics: A comparative analysis of Lévy flight and stigmergy methods

Utilizing swarm robotics techniques can significantly enhance the efficiency of exploring and mapping hazardous environments, such as nuclear sites. Instead of relying on a single robot for exploration, employing multiple robots working in coordination allows for fast coverage and more comprehensive data collection. In this study, bio-inspired algorithms, specifically Lévy flight and stigmergy, are utilized to guide the robots' movements. The Lévy flight algorithm mimics the movement patterns observed in animals like sharks and honeybees during their search for food, while stigmergy involves indirect communication between agents through environmental traces. By integrating these algorithms with swarm robotics, the robots effectively explore radioactive environments, gather data, and generate detailed maps of the area. Our research delves into various aspects of exploration, including the influence of the number of deployed robots and their exposure to radiation. Comparative analysis reveals the efficacy of stigmergy as a superior approach for guiding swarm robot movements in radioactive environments. This study underscores the significant potential of employing collective robotics for exploration tasks in nuclear scenarios, highlighting the promising applications of swarm intelligence in enhancing safety and efficiency in hazardous environments.

Applying the "SOTEC" framework of sociotechnical risk analysis to the development of an autonomous robot swarm for a public cloakroom.

The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex "nontechnical" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking.

Edge-Light: Exploiting Luminescent Solar Concentrators for Ambient Light Communication

A recent advance in embedded Internet of Things (IoT) exploits ambient light for wireless communication. This new paradigm enables highly efficient links via simple light modulation, but the design space has a fundamental constraint: in most State of the Art (SoA) studies, the link can only follow the propagation direction of ambient light. Consider, for example, a swarm of drones and ground robots that want to communicate with sunlight. Drone-to-robot communication could be possible because sunlight travels downwards from the air (drone) to the ground (robot), allowing drones to modulate light to send information to robots beneath them. Robot-to-robot communication, however, is not possible because sunlight does not travel sideways (parallel to the ground). To allow 'lateral communication' with ambient light, we propose using Luminescent Solar Concentrators (LSC). These optical components receive ambient light on their surface and re-direct part of the spectra towards their edges. Considering this optical property of LSC, our work has three main contributions. First, we benchmark various optical properties of LSC to assess their performance for ambient light communication. Second, we combine LSC with liquid crystal (LC) shutters to form lateral links with ambient light. Third, we test our links indoors and outdoors with artificial and natural ambient light, by enhancing two robots with our LSC transceivers and showing that they can exchange basic commands and coordinate tasks by communicating only with sunlight.

Studying data loss, nonlinearity, and modulation effects in drone swarm channels with artificial intelligence

Studying data loss, nonlinearity, and modulation effects in drone swarm channels with artificial intelligence

Camera-based mapping in search-and-rescue via flying and ground robot teams

Search and rescue (SaR) is challenging, due to the unknown environmental situation after disasters occur. Robotics has become indispensable for precise mapping of the environment and for locating the victims. Combining flying and ground robots more effectively serves this purpose, due to their complementary features in terms of viewpoint and maneuvering. To this end, a novel, cost-effective framework for mapping unknown environments is introduced that leverages You Only Look Once and video streams transmitted by a ground and a flying robot. The integrated mapping approach is for performing three crucial SaR tasks: localizing the victims, i.e., determining their position in the environment and their body pose, tracking the moving victims, and providing a map of the ground elevation that assists both the ground robot and the SaR crew in navigating the SaR environment. In real-life experiments at the CyberZoo of the Delft University of Technology, the framework proved very effective and precise for all these tasks, particularly in occluded and complex environments.

Noisy Circumnutations Facilitate Self-Organized Shade Avoidance in Sunflowers

Circumnutations are widespread in plants and typically associated with exploratory movements; however, a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering 3 orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a Langevin-type parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution represents a sharp transition in the force-noise ratio, conferring advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements and establish a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. Since plants move by growing, space and time are coupled, and dynamics of self-organization lead to emergent 3D patterns. As such, this system provides conceptual insight for other interacting growth-driven systems such as fungal hyphae, neurons and self-growing robots, as well as active matter systems where agents interact with past trajectories of their counterparts, such as stigmergy in social insects. This foundational insight has implications in statistical physics, ecological dynamics, agriculture, and even swarm robotics. Published by the American Physical Society 2024

Industrial Metaverse: A proactive human-robot collaboration perspective

Human-centricity, sustainability, and resilience are becoming core values in modern manufacturing, with human–robot collaboration (HRC) in high demand for flexible automation. However, human–robotic swarms are typically designed to target one specific procedure and cannot fully share their autonomy. The Metaverse, characterized by socialized avatars in a virtual-physical fused world, holds the promise of Proactive HRC. In line with this evolutionary roadmap, this paper presents a futuristic perspective on the industrial Metaverse for Proactive HRC and identifies its six embodiments. A representative universe that supports online and offline human users/operators in the design, machining, and maintenance of aeroengine turbine blades is introduced to spark and accelerate future implementation of the industrial Metaverse for Proactive HRC. The current challenges and future opportunities of this paradigm are also highlighted. It is hoped that this work can attract further investigation and discussions, providing useful insights to both academic and industrial practitioners in smart manufacturing.

Perceptions of Autonomous Robot Teammates During Field Operations

Perceptions of Autonomous Robot Teammates During Field Operations

Design Ideation for Autonomous Reproductive Worm Robot: Inheritance from Biological Worm C.elegans

The vast domain of Bio-inspired Robotics has been nourished with several key-themes of design over the years and those are, no doubt conceptually brilliant. The size effect of such bio-robotic gadgets was also tackled through modern-age manufacturing as well as assembly technologies and we have got experimental gadgets like worm robots as outcome. However, in all such prototypes of miniature bio-robots and/or worm robots, the essence of biological science has been missing to date. The concept of bio-inspired team robots having near-identical features, symbolizing biological off-springs was not explored hitherto. We are attempting to provide a novel autonomous assistive robotic system having ‘Mother’ and ‘Daughter(s)’ for use in agricultural fields. Our design has been nucleated from a famous biological worm, namely, Caenorhabditis elegans (C.elegans) which has a fully-developed reproductive system, notwithstanding its length (~ 1 mm.) or even the diameter (~ 50 microns). In this paper, we will report the technological ideation design of a representative ‘Mother-Daughter’ Worm Robotic system by naturally inheriting the reproductive mechanism of the biological C.elegans worm (barring the size effect).