Team Of Robots Research Articles

Counterfactual rewards promote collective transport using individually controlled swarm microrobots.

Swarm robots offer fascinating opportunities to perform complex tasks beyond the capabilities of individual machines. Just as a swarm of ants collectively moves large objects, similar functions can emerge within a group of robots through individual strategies based on local sensing. However, realizing collective functions with individually controlled microrobots is particularly challenging because of their micrometer size, large number of degrees of freedom, strong thermal noise relative to the propulsion speed, and complex physical coupling between neighboring microrobots. Here, we implemented multiagent reinforcement learning (MARL) to generate a control strategy for up to 200 microrobots whose motions are individually controlled by laser spots. During the learning process, we used so-called counterfactual rewards that automatically assign credit to the individual microrobots, which allows fast and unbiased training. With the help of this efficient reward scheme, swarm microrobots learn to collectively transport a large cargo object to an arbitrary position and orientation, similar to ant swarms. We show that this flexible and versatile swarm robotic system is robust to variations in group size, the presence of malfunctioning units, and environmental noise. In addition, we let the robot swarms manipulate multiple objects simultaneously in a demonstration experiment, highlighting the benefits of distributed control and independent microrobot motion. Control strategies such as ours can potentially enable complex and automated assembly of mobile micromachines, programmable drug delivery capsules, and other advanced lab-on-a-chip applications.

Non-invasive eye tracking and retinal view reconstruction in free swimming schooling fish

Eye tracking has emerged as a key method for understanding how animals process visual information, identifying crucial elements of perception and attention. Traditional fish eye tracking often alters animal behavior due to invasive techniques, while non-invasive methods are limited to either 2D tracking or restricting animals after training. Our study introduces a non-invasive technique for tracking and reconstructing the retinal view of free-swimming fish in a large 3D arena without behavioral training. Using 3D fish bodymeshes reconstructed by DeepShapeKit, our method integrates multiple camera angles, deep learning for 3D fish posture reconstruction, perspective transformation, and eye tracking. We evaluated our approach using data from two fish swimming in a flow tank, captured from two perpendicular viewpoints, and validated its accuracy using human-labeled and synthesized ground truth data. Our analysis of eye movements and retinal view reconstruction within leader-follower schooling behavior reveals that fish exhibit negatively synchronised eye movements and focus on neighbors centered in the retinal view. These findings are consistent with previous studies on schooling fish, providing a further, indirect, validation of our method. Our approach offers new insights into animal attention in naturalistic settings and potentially has broader implications for studying collective behavior and advancing swarm robotics.

Mixed‐Integer Motion Planning for Nonholonomic Robots Under Visible Light Communication Constraints

ABSTRACTThis work is concerned with the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. Recently, multi‐robot systems operating with VLC networks have been demonstrated as an attractive solution for inspection tasks in pipelines and underground facilities. However, there are still no established methodologies to automate their operation. We address this problem with an optimization framework based on Mixed‐Integer Linear Programming (MILP) to design a motion planner that safely coordinates the robot team while jointly addressing the nonlinearity of the robots' dynamics, the directed line‐of‐sight requirement for VLC connections, and the connectivity maintenance of the ensuing directed networks. We propose a novel method to encode the nonlinear dynamics of nonholonomic robots into the MILP formulation and demonstrate through comparative trials that, although the resulting optimization search space is more restricted, the approach still outperforms mixed‐integer nonlinear programming in terms of optimization time and solution quality. The efficacy of the proposal is also demonstrated through realistic simulations of the Turtlebot3 robot platform.

Advancing Multi-Robot Path Planning through Nature Inspired Algorithms

In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.

Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 h) were conducted based on the DARPA Subterranean Challenge. Missions involved deploying a heterogeneous team of robots to locate and identify artefacts such as climbing rope, drills and a mannequin representing a human survivor. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human interventions included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items ( \(+\) 10.52%), traversed more distance ( \(+\) 12.71%), covered more unique ground ( \(+\) 10.56%), and longer time between safety-related events (34%). In routine conditions, both condition scores were comparable for artefacts, distance and coverage. Human-Robot Teams were faster at finding the first artefact but slower to respond to information from the robot team. Overall, operators contribute to mission-based outcomes, help to overcome environmental situations that can impede progress, and can assist robots to recover faster from difficult events.

Occlusion-based object transportation around obstacles with a swarm of miniature robots

Occlusion-based object transportation around obstacles with a swarm of miniature robots

Human prior knowledge estimation from movement cues for information-based control of mobile robots during search

Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.

Variable-State-Trigger: A Formal Model of Smart Contracts Based on Conditional Response and Finite State Automata and Its Application

As one of the technical elements supporting the blockchain to realize decentralized autonomous organizations, smart contracts are crucial for understanding the inherent properties of blockchain systems through formal research. Most existing formal models of smart contracts focus primarily on static properties, lacking the depiction of dynamic processes such as conditional responses and internal state migration during contract execution, which complicates effective supervision. In this paper, a novel formal model of smart contracts, based on finite state automata, named Variable-State-Trigger (VST), is proposed, which presents state migration and conditional response mechanisms during smart contract execution. The VST model is verified to portray both the static and dynamic properties of smart contracts, providing new avenues for effective supervision during contract execution. Moreover, the VST model is used to illustrate the life cycle of a UAV mission smart contract, demonstrating its feasibility. With the growing integration of blockchain and emerging technologies, smart contracts have found applications in various fields, including drone swarm coordination. In this context, smart contracts enable secure, decentralized management and automation of interactions between autonomous drones, ensuring compliance with predefined conditions and enhancing the reliability of complex drone operations. The VST model can be extended to such applications, offering dynamic supervision and enhancing the coordination efficiency in decentralized autonomous drone systems.

Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

ABSTRACTSwarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.

Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking

This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results.

Gathering on a circle with limited visibility by anonymous oblivious robots

A swarm of anonymous oblivious mobile robots, operating in deterministic Look-Compute-Move cycles, is confined within a circular track. All robots agree on the clockwise direction (chirality), they are activated by an adversarial semi-synchronous scheduler (SSYNCH), and an active robot always reaches the destination point it computes (rigidity). Robots have limited visibility: each robot can see only the points on the circle that have an angular distance strictly smaller than a constant ϑ from the robot's current location, where 0<ϑ≤π (angles are expressed in radians).We study the Gathering problem for such a swarm of robots: that is, all robots are initially in distinct locations on the circle, and their task is to reach the same point on the circle in a finite number of turns, regardless of the way they are activated by the scheduler. Note that, due to the anonymity of the robots, this task is impossible if the initial configuration is rotationally symmetric; hence, we have to make the assumption that the initial configuration be rotationally asymmetric.We prove that, if ϑ=π (i.e., each robot can see the entire circle except its antipodal point), there is a distributed algorithm that solves the Gathering problem for swarms of any size. By contrast, we also prove that, if ϑ≤π/2, no distributed algorithm solves the Gathering problem, regardless of the size of the swarm, even under the assumption that the initial configuration is rotationally asymmetric and the visibility graph of the robots is connected.The latter impossibility result relies on a probabilistic technique based on random perturbations, which is novel in the context of anonymous mobile robots. Such a technique is of independent interest, and immediately applies to other Pattern-Formation problems.

Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training.

Efficiency of Energy Exchange Strategies in Model Bacteriabot Populations

Micro/nanorobotics is becoming part of the future of medicine. One of the most efficient approaches to the construction of small medical robots is to base them on unicellular organisms. This approach inherently allows for obtaining complex capabilities, such as motility or environmental resistance. Single-celled organisms usually live in groups and are known to interact in many ways (matter, energy, and information), paving the way for potentially beneficial emergent effects. One such naturally expected effect is an increase in the sustainability of a population as a result of a more even redistribution of energy within the population. Our in silico experiments show that under harsh conditions, such as resource scarcity and a rapidly changing environment, altruistic energy exchange (supplying energy to weaker agents) can indeed markedly increase the sustainability of model bacteriabot groups, potentially increasing the efficiency of treatment. Although our work is limited exclusively to the development and use of a phenomenological computer model, we consider our results to be an important argument in favor of practical efforts aimed at implementing altruistic energy exchange strategies in real swarms of single-cell medical robots.

GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space

In many real-world scenarios, tasks involve coordinating multiple agents, such as managing robot clusters, drone swarms, and autonomous vehicles. These tasks are commonly addressed using Multi-Agent Reinforcement Learning (MARL). However, existing MARL algorithms often lack foresight regarding the number and types of agents involved, requiring agents to generalize across various task configurations. This may lead to suboptimal performance due to underestimated action values and the selection of less effective joint policies. To address these challenges, we propose a novel multi-agent deep reinforcement learning framework, called multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space (GDT). GDT utilizes a group mesh topology to interconnect the local action value functions of each agent, enabling effective coordination and knowledge sharing among agents. By computing three different interpretations of action value functions, GDT overcomes monotonicity constraints and derives more effective overall action value functions. Additionally, GDT groups agents with high similarity to facilitate parameter sharing, thereby enhancing knowledge transfer and generalization across different scenarios. Furthermore, GDT introduces a strategy regularization method for optimal exploration of multiple action spaces. This method assigns each agent an independent entropy temperature during exploration, enabling agents to efficiently explore potential actions and approximate total state values. Experimental results demonstrate that our approach, termed GDT, significantly outperforms state-of-the-art algorithms on Google Research Football (GRF) and the StarCraft Multi-Agent Challenge (SMAC). Particularly in SMAC tasks, GDT achieves a success rate of nearly 100% across almost all Hard Map and Super Hard Map scenarios. Additionally, we validate the effectiveness of our algorithm on Non-monotonic Matrix Games.

Self-organizing nervous systems for robot swarms.

We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of n independent robots could transform into a single n-robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we showed that sensing, actuation, and decision-making can be coordinated in a locally centralized way without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions-including binary decision-making and search and rescue-we demonstrated that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions were conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrated the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator and demonstrated several types of system fault tolerance in simulation and reality.

Adversarial imitation learning with deep attention network for swarm systems

Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This paper addresses this issue by proposing an imitation learning method, which derives individual policies from collective behavior data. The approach leverages an adversarial imitation learning framework, with a deep attention network serving as the individual policy network. Our method successfully imitates three distinct collective behaviors. Utilizing the ease of analysis provided by the deep attention network, we have verified that the individual policies underlying a certain collective behavior are not unique. Additionally, we have analyzed the different individual policies discovered. Lastly, we validate the applicability of the proposed method in designing policies for swarm robots through practical implementation on swarm robots.

Emergent Dynamic Formation through Optical Interactions in a Robot Swarm

Self‐organized formation is a key direction in swarm robotics. It is still challenging to design local interactions toward desired global formations and even more challenging for dynamic formations in a physical robot swarm system. Herein, a self‐organized method for emergent dynamic circling formation in a robot swarm through optical interactions is proposed. First, this method is quantitatively modeled based on the geometrical relations among robots. This model is further adjusted according to the characteristics of the robot swarm system. To demonstrate the effectiveness of this model, the effects of three key parameters of this model are tested on the size and disorder level of the emergent dynamic circling formation. The experimental results are consistent with the model predictions. Overall, a robot swarm system, in the physical environment, is quantitatively controlled to emerge a dynamic circling formation in this article. This work advances the swarm robotics for quantitatively designing local interactions among robots to reliably emerge dynamic global patterns.

A novel memetic algorithm for distributed shape formation of swarm robots with both acceleration and velocity constraints

A novel memetic algorithm for distributed shape formation of swarm robots with both acceleration and velocity constraints

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

Speech communication interference in the robotic operating room.

Miscommunication in the OR is a threat to patient safety and surgical efficiency. Our objective was to measure the frequency and causes of communication interference between robotic team members. We observed 78 robotic surgeries over 215h. 65.4% were General Surgery, most commonly cholecystectomy, identifying Speech Communication Interference (SCI) events, defined as "surgery-related group discourse that is disrupted according to the goals of the communication or the physical and situational context of the exchange". We noted the causes and strategies to correct the miscommunication, near misses, and case delays associated with each SCI event. Post-surgery interviews supported observations and were analyzed thematically. Overall, we observed 687 SCI events (mean 8.8 ± 6.5 per case, 3.2 per hour), ranging from one to 28 per case. 48 (7.0%) occurred during docking and 136 (19.8%) occurred during a critical moment. The most common causes were concurrent tasks (66.1%); loud noises (10.8%) from patient cart, lightbox fan, and suction machine; and overlapping conversations (4.2%). 94.8% resulted in a case delay. These events distracted from monitoring patient safety and resulted in near misses. Mitigating strategies included leaning out of the surgeon console to repeat the message and employing a messenger. These findings help characterize miscommunication in robotic surgery. Possible interventions include microphones and headsets, positioning the surgeon console closer to the bedside, moving loud equipment further away, and upgrading the patient cart speaker.