Team Of Robots Research Articles

Distributed Control within a Trapezoid Virtual Tube Containing Obstacles for Robotic Swarms Subject to Speed Constraints

Distributed Control within a Trapezoid Virtual Tube Containing Obstacles for Robotic Swarms Subject to Speed Constraints

Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance

Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance

Game-Based Approximate Optimal Motion Planning for Safe Human-Swarm Interaction.

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.

Speed and Density Planning for a Speed- Constrained Robot Swarm Through a Virtual Tube

Speed and Density Planning for a Speed- Constrained Robot Swarm Through a Virtual Tube

Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination

Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination

Cooperative planning for physically interacting heterogeneous robots.

Heterogeneous multi-agent systems can be deployed to complete a variety of tasks, including some that are impossible using a single generic modality. This paper introduces an approach to solving the problem of cooperative behavior planning in small heterogeneous robot teams where members can both function independently as well as physically interact with each other in ways that give rise to additional functionality. This approach enables, for the first time, the cooperative completion of tasks that are infeasible when using any single modality from those agents comprising the team.

Implementation of an Artificially Empathetic Robot Swarm.

This paper presents a novel framework for integrating artificial empathy into robot swarms to improve communication and cooperation. The proposed model uses fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. By utilizing similarity measures, the model compares states, enabling empathetic reasoning for synchronized swarm behavior. The paper presents a practical application example that demonstrates the efficacy of the model in a robot swarm working toward a common goal. The evaluation methodology involves the open-source physical-based experimentation platform (OPEP), which emphasizes empirical validation in real-world scenarios. The paper proposes a transitional environment that enables automated and repeatable execution of experiments on a swarm of robots using physical devices.

A Survey of Applications of Blockchain in Collective Decision-Making Scenarios in Swarm Robotics

Blockchain is a distributed ledger that was introduced to decentralize monetary systems. However, with time, the applications of blockchain in different realms have been identified. Swarm robotics is a field that combines swarm intelligence and robotics to solve real-world problems that cannot be solved by monolithic robots. Collective decision-making is one of the major behaviors implemented by swarm robotics. This study analyzes existing literature on the applications of blockchain in the collective decision-making scenarios in swarm robotics. Consequently, this study introduces a novel taxonomy to study the different applications effectively. The taxonomy categorizes existing literature into (i) application of blockchain in other areas of swarm robotics, (ii) application of blockchain in continuous collective decision-making scenarios, (iii) application of blockchain in discrete collective decision-making scenarios, (iv) application of blockchain in other discrete collective decision-making scenarios, and (v) application of blockchain in the collective perception scenario. Finally, the limitations of existing work such as excessive resource consumption and violation of swarm robotics principles are discussed.

Алгоритм координации роя автономных мобильных роботов

An algorithm is presented that coordinates the movements of multiple robots, ensuring that their positions remain relative to each other as they move. Also, this algorithm allows it to adapt to changing conditions, such as the presence of obstacles. To test the effectiveness of the proposed algorithm, a swarm of nine robots was simulated. The results showed that the algorithm successfully allowed the robots to maintain the desired formation while moving along a given route. Based on these results, it can be concluded that the proposed algorithm is a promising approach for coordinating the movement of a swarm of robots. The practical applications of this algorithm are numerous, including military missions, search and rescue operations, and vehicle control systems. The algorithm can also be extended to larger robot swarms or other types of autonomous systems. In addition, it is possible to further develop this algorithm to solve more complex problems related to robot swarm motion in a dynamic and unpredictable environment.

An integrated Design Methodology for Swarm Robotics using Model-Based Systems Engineering and Robot Operating System

In swarm robotics, robots solve problems using collective behaviors similar to those observed in natural systems, such as birds, bees or fish. They determine their collective behavior through several simple interactions. However, the behavior of swarms emerging from local interactions remains difficult to predict. When trying to design swarm robotic systems for real applications, researchers are confronted with a wide range of software and hardware challenges in the different phases of the design process; problems related to the modeling of swarm systems, related to simulation, related to implementation on software and even on real systems, etc. Despite the increasing popularity of swarm robotics, designing effective and scalable swarm systems remains a challenging task due to the complex interactions between the individual robots and the environment. Therefore, there is a need for a systematic and comprehensive methodology that can guide designers in developing swarm robotics systems that are efficient, reliable, and adaptable to different scenarios. Our main contribution is to propose a new integrated methodology for the development of swarm robot systems. This methodology is based on modeling with Model-Based Systems Engineering method (MBSE) to specify the requirements and the collective behaviors of the swarms, then on the verification of the developed models and finally on the validation of the swarm system by physical prototyping with real robots. Our contribution also focuses on the development of a new SysML profile using the Domain Specific Language (DSL) that we call SwarmML to customize the functional and structural modeling of a swarm system (properties and attributes of the swarm). Two case studies are applied to validate our methodology; a case study of an aggregation of a swarm of robots and a case study of a collaborative simultaneous localization and mapping application (C-SLAM) performed by a swarm of Turtlebots. The novelty of this proposed methodology is the combination of SysML and Robot Operating System (ROS) to address the management of traceability between the different levels of swarm system design, in order to achieve functional, physical and software integration.

Swarm Robots in CBRN Decontamination: Enhancing Efficiency and Safety

In this paper, we explore the integration of swarm robots in CBRN (Chemical, Biological, Radiological, and Nuclear) decontamination processes. Swarm robots, known for their collaborative and decentralized nature, hold promise in improving the efficiency and safety of decontamination operations. Our study provides an overview of swarm robots and CBRN decontamination, highlighting the challenges and requirements associated with this critical task. We delve into the specific applications of swarm robots in CBRN decontamination, discussing their design considerations, operational aspects, and the advantages they bring to the process. To evaluate the efficacy of swarm robot systems, we present real-world case studies encompassing various scenarios. Furthermore, we address the remaining challenges in this field and explore future directions by identifying emerging technologies and techniques. Our research aims to contribute to the existing knowledge base, fostering a deeper understanding of swarm robot based CBRN decontamination and inspiring further advancements in this evolving domain.

A Survey on Swarm Robotics for Area Coverage Problem

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

Marine algae inspired dispersion of swarm robots with binary sensory information

The dynamics of swarm robotic systems are complex and often nonlinear. One key issue is to design the controllers of a large number of simple, low-cost robots so that emergence can be observed. This paper presents a sensor and computation-friendly controller for swarm robotic systems inspired by the mechanisms observed in algae. The aim is to achieve uniform dispersion of robots by mimicking the circular movement observed in marine algae systems. The proposed controller utilizes binary sensory information (i.e., see or not see) to guide the robots’ motion. By moving circularly and switching the radii based on the perception of other robots in their line of sight, the robots imitate the repulsion behavior observed in algae. The controller relies solely on binary-state sensory input, eliminating the need for additional memory or communication. Up to 1024 simulated robots are used to validate the effectiveness of the dispersion controller, while experiments with 30 physical robots demonstrate the feasibility of the proposed approach.

A novel hedge algebra formation control for mobile robots

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra’s significant potential in the mobile robots’ domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain.

The Sound of Swarm. Auditory Description of Swarm Robotic Movements

Movements of robots in a swarm can be mapped to sounds, highlighting the group behavior through the coordinated and simultaneous variations of musical parameters across time. The vice versa is also possible: Sound parameters can be mapped to robotic motion parameters, giving instructions through sound. In this article, we first develop a theoretical framework to relate musical parameters such as pitch, timbre, loudness, and articulation (for each time) with robotic parameters such as position, identity, motor status, and sensor status. We propose a definition of musical spaces as Hilbert spaces and musical paths between parameters as elements of bigroupoids, generalizing existing conceptions of musical spaces. The use of Hilbert spaces allows us to build up quantum representations of musical states, inheriting quantum computing resources, already used for robotic swarms. We present the theoretical framework and then some case studies as toy examples. In particular, we discuss a 2D video and matrix simulation with two robo-caterpillars; a 2D simulation of 10 robo-ants with Webots; a 3D simulation of three robo-fish in an underwater search-and-rescue mission.

Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration

OCCUPATIONAL APPLICATIONS An understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (HRC) for a material sorting task. When compared to a sequential mode of interaction, the simultaneous mode resulted in significantly higher perceptions of fluency and engagement, primarily by reducing human idle time. These observations were complemented by significant changes in physiological responses, such as ECG entropy and low frequency power. These responses could predict fluency and engagement with accuracies of 90 and 97%, respectively. Notably, the perception of fluency and preferred mode of interaction were influenced by individual preferences. Hence, it is crucial to consider both physiological responses and user preferences when designing HRC systems, to ensure a positive experience with the robot teammate and to foster engagement in long-term teamwork. Furthermore, these signals can be obtained using a single robust, low-cost, and comfortable sensor.

Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters.

Distributed Gossip-Triggered Control for Robot Swarms With Limited Communication Range

Robot swarms usually work in uncertain and unstructured environments. It is challenging to control the robot swarm to accomplish multiple tasks in the shortest possible time when the task operation duration is unknown, and the communication network is time-varied and possibly disconnected. To this end, a new distributed gossip-triggered control (DGTC) method is proposed. Robots mimic human gossip behavior to disseminate and aggregate local information while triggering decisions to reduce task conflicts and system costs. The swarm dynamics, including discrete decisions and continuous trajectory, are formulated as a giant switched system. On this basis, the system stability and convergence of the DGTC are proved, and a criterion to balance the convergence rate and communication burden is given. More importantly, robot swarms can balance the workload between individuals by self-regulating the number of active agents to match the remaining tasks, thus ensuring a tight schedule without any time knowledge. Simulation results demonstrate the advantages of DGTC in generating tight schedules, and experiments conducted with Mecanum cars validate the effectiveness in dealing with time uncertainties.

An overview of emerging trends in robotics and automation

Robotics has experienced significant growth and development over the past few decades, with the first industrial robots introduced in the 1950s. As technology advances, robotics has become more intelligent, smart, and flexible, with the introduction of artificial intelligence (AI) and machine learning (ML). Robots are now being integrated into various fields, including healthcare, agriculture, transportation, and space exploration. Robots are set to revolutionize our daily lives, transforming interactions and work processes as technology advances. Emerging trends in robotics, such as AI and ML, soft robotics, and swarm robotics, can transform industries and improve efficiency. The future of robotics and automation promises safer workplaces, improved healthcare, and enhanced environmental sustainability. Society must adapt to this changing landscape, requiring continuous learning and upskilling to remain relevant in the workforce.

Evolutionary Algorithm-Based Attack Strategy With Swarm Robots in Denied Environments

An attack-defense confrontation problem arises from a robot swarm attacking a territory protected by another one. In denied environments, global positioning and communication are hardly available. It becomes difficult for a swarm to realize collaboration and handle confrontation against another. Commonly-used deep reinforcement learning relies on pre-training, which is time-consuming and has strong environmental dependence, especially in denied environments. To study attack strategies in denied environments, this work proposes a novel Evolutionary Algorithm-based Attack Strategy with Swarm Robots for the first time. Each robot obtains its situation information through perceiving its nearby peers and enemies. Such information is utilized to evaluate the benefits or threats of a robot’s next perceptible attack positions. Then, each robot uses the evolutionary algorithm to optimize its fitness function and searches for its optimal position. A collision avoidance strategy is integrated into the algorithm. Hence, a robot swarm realizes collaboration and handles confrontation as long as each robot can sense its surroundings. They utilize their own sensors to detect others locally without using global positioning and communication devices. The experimental analyses show that the evolutionary algorithm-based attack strategy has better scalability and more potential in solving large-scale confrontational problems than the deep reinforcement learning-based algorithms. Rationales of the proposed method is presented to verify the great capability of the proposed method.