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Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain‐specific artificial intelligence is more reliable, energy‐efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient. [ABSTRACT FROM AUTHOR]

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

In response to the challenging conditions that arise after natural disasters, multi-robot systems are utilized as alternatives to humans for searching and rescuing victims. Exploring unknown environments is crucial in mobile robotics, serving as a foundational stage for applications such as search and rescue, cleaning tasks, and foraging. In our study, we introduced a novel search strategy for multi-robot search and rescue operations. This strategy draws inspiration from the hunting behavior of penguins and combines the Penguin Search Optimization Algorithm with the Random Walk Algorithm to regulate the global and local search behaviors of the robots. To assess the strategy's effectiveness, we implemented it in the ARGoS multi-robot simulator and conducted a series of experiments. The results clearly demonstrate the efficiency and effectiveness of our proposed search strategy. [ABSTRACT FROM AUTHOR]

One of the main challenges in swarm robotics is to achieve robust and scalable flocking, such that large numbers of robots can move together in a coordinated and cohesive manner while avoiding obstacles or threats. Flocking models in swarm robotic systems typically use reactive behaviors, such as cohesion, alignment, and avoidance. The use of potential fields has enabled the derivation of reactive control laws using obstacles and neighboring robots as sources of force for flocking. However, reactive behaviors, especially when a multitude of them are simultaneously active, as in the case of flocking, are prone to cause collisions or inefficient motion within the flock due to its short-sighted approach. Approaches that aimed to generate smoother and optimum flocking, such as the use of model predictive control, would either require centralized coordination, or distributed coordination which requires low-latency and high-bandwidth communication requirements within the swarm as well as high computational resources. In this paper, we present a predictive search model that can generate smooth and safe flocking of robotic swarms in the presence of obstacles by taking into account the predicted states of other robots in a computationally efficient way. We tested the proposed model in environments with static and dynamic obstacles and compared its performance with a potential field flocking model in simulation. The results show that the predictive search model can generate smoother and faster flocking in swarm robotic systems in the presence of static and dynamic obstacles. Furthermore, we tested the predictive search model with different numbers of robots in environments with static obstacles in simulations and demonstrated that it is scalable to large swarm sizes. The performance of the predictive search model is also validated on a swarm of six quadcopters indoors in the presence of static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: 1) robot; 2) algorithm; and 3) consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated. [ABSTRACT FROM AUTHOR]

Image dehazing and object detection are two different research areas that play a vital role in machine learning. When merged together and implemented in real‐time, it is a boon in the field of artificial intelligence, specifically robotics. Object detection and tracking are two of the major implementations in almost the entire robot's training and learning. The learning of the robot depends on the images; these images can be camera‐captured images or a pretrained data set. Real‐time outdoor images clicked in bad weather conditions, such as mist, haze, smog, and fog, often suffer from poor visibility, and the consequences are incorrect results and hence an unexpected robot's behavior. To overcome these consequences, we have presented a novel approach to object detection and identification during adverse weather conditions. This method is proposed to be implemented in a real‐time environment to monitor animal behavior near railway tracks during fog, haze, and smog. This is not limited to specific application areas but can be used to identify endangered species and take active steps to save them from mishap. The deployment is done in a real‐time indoor environment using Tortoisebot mobile robot with a robot operating system framework. [ABSTRACT FROM AUTHOR]

Biologically inspired collective decision-making algorithms show promise for implementing spatially distributed searching tasks with robotic systems. One example is the best-of-N problem in which a collective must search an environment for an unknown number of sites and select the best option. Real-world robotic deployments must achieve acceptable success rates and execution times across a wide variety of environmental conditions, a property known as resilience. Existing experiments for the best-of-N problem have not explicitly examined how the site layout affects a collective's performance and resilience. Two novel resilience metrics are used to compare algorithmic performance and resilience between evenly distributed, obstructed, or unobstructed uneven site configurations. Obstructing the highest valued site negatively affected selection accuracy for both algorithms, while uneven site distribution had no effect on either algorithm's resilience. The results also illuminate the distinction between absolute resilience as measured against an objective standard, and relative resilience used to compare an algorithm's performance across different operating conditions. [ABSTRACT FROM AUTHOR]

In rational decision-making processes, the information interaction among individual robots is a critical factor influencing system stability. We establish a game-theoretic model based on mutual information to address division of labor decision-making and stability issues arising from differential information interaction among swarm robots. Firstly, a mutual information model is employed to measure the information interaction among robots and analyze its influence on the behavior of individual robots. Secondly, employing the Cournot model and the Stackelberg model, we model the diverse decision-making behaviors of swarm robots influenced by discrepancies in mutual information. The intricate decision dynamics exhibited by the system under the disparity mutual information values during the game process, along with the stability of Nash equilibrium points, are analyzed. Finally, dynamic complexity simulations of the game models are simulated under the disparity mutual information values: (1) When ν1 of the game model varies within a certain range, the Nash equilibrium point loses stability and enters a chaotic state. (2) As I(X;Y) increases, the decision-making pattern of robots transitions gradually from the Cournot game to the Stackelberg game. Concurrently, the sensitivity of swarm robotics systems to changes in decision parameter decreases, reducing the likelihood of the system entering a chaotic state. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of formation control and navigation in swarms of mobile robots, presenting an innovative algorithm that integrates behavioral approaches with fuzzy logic to calculate behavior weights for Efficient, adaptive coordination dynamically. Using a leader-follower mechanism enhanced by a virtual leader, the algorithm enables coordinated swarm movement in complex environments. The methodology combines fuzzy logic with a behavioral strategy, allowing mobile robots to adjust their actions based on environmental cues and swarm dynamics without predefned paths. Simulated experiments show the algorithm's capability to maintain formation integrity and navigate obstacles, significantly improving swarm adaptability and operational efficiency. The findings suggest a promising direction for developing intelligent mobile robotic systems capable of complex, autonomous tasks, contributing to the field of swarm robotics with a novel approach to formation control and navigation that balances individual autonomy with collective behavior. [ABSTRACT FROM AUTHOR]

Swarm robotics is the study of how a large number of relatively simple physically embodied robots can be designed such that a desired collective behavior emerges from local interactions. Furthermore, reinforcement learning (RL) is a promising approach for training robotic swarm controllers. However, the conventional RL approach suffers from the sparse reward problem in some complex tasks, such as key-to-door tasks. In this study, we applied hierarchical imitation learning to train a robotic swarm to address a key-to-door transport task with sparse rewards. The results demonstrate that the proposed approach outperforms the conventional RL method. Moreover, the proposed method outperforms the conventional hierarchical RL method in its ability to adapt to changes in the training environment. [ABSTRACT FROM AUTHOR]

In this study, we introduce a deep Q-network agent utilizing a dueling architecture to refine the valuation of actions through separate estimations of the state-value and action-value functions, adapted to facilitate concurrent multi-agent operations within a shared environment. Inspired by the self-organized, decentralized cooperation observed in natural swarms, this study uniquely integrates a centralized mechanism, or a centralized critic. This enhances performance and coherence in decision-making within the multi-agent system. This hybrid approach enables agents to execute informed and optimized decisions by considering the actions of their counterparts while maintaining an element of collective and flexible task-information sharing, thereby presenting a groundbreaking framework for cooperation and information sharing in swarm robot systems. To augment the communication capabilities, we employ low-power wide-area networks, or Long Range (LoRa), which are characterized by their low power consumption and long-range communication abilities, facilitating the sharing of task information and reducing the load on individual robots. The aim is to leverage LoRa as a communication platform to construct a cooperative algorithm that enables efficient task-information sharing among groups. This can provide innovative solutions and promote effective cooperation and communication within multi-agent systems, with significant implications for industrial and exploratory robots. In conclusion, by integrating a centralized system into the proposed model, this approach successfully enhances the performance of multi-agent systems in real-world applications, offering a balanced synergy between decentralized flexibility and centralized control. [ABSTRACT FROM AUTHOR]

Patrolling represents a potential application area for multi-robot systems, as it can enable efficient surveillance. A key aspect in facilitating the real-world applications of such missions is the enhancement of situation awareness of the base station (BS), in addition to ensuring well-coordinated patrol behavior. This paper addresses this requirement by proposing a layered patrol algorithm designed to maintain network connectivity with the BS. The novelty of this research lies in the distributed nature of the algorithm, despite the presence of the BS. Each robot independently determines its behavior based on local information while concurrently preserving connectivity to the BS. Additionally, this study introduces a novel performance metric to assess the situation awareness of the BS, focusing on the algorithm's ability to provide prompt information about mission progress. Simulated missions revealed that the proposed algorithm outperformed existing algorithms, visited locations of interest more frequently and comprehensively, and provided the BS with improved situation awareness. Enhancing situation awareness may enable human operators to quickly gain insights into the system's behavior based on mission progress, allowing for timely interventions if necessary. This capability contributes to improving human trust in autonomous systems. [ABSTRACT FROM AUTHOR]

Construction robots employ cutting-edge technology to perform tasks more accurately than traditional construction workers, producing higher-quality results and fewer mistakes. Moreover, although construction robotics is a demanding topic in construction sector research, more review studies that track and anticipate adoption trends are required in the construction sector. This study aims to bridge this gap by identifying the adoption challenges and limitations of construction robots and the opportunities offered to the construction sector. To achieve this aim, the study adopts a systematic literature review approach using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Additionally, the systematic literature review focuses on the framework for categorizing technological advances and potential trends in development over the past decade. The review results reveal that: (a) current robotic technology covered four critical perspectives including perception, mobility, manipulation, and collaboration; (b) promoting the sector requires attention to safety and ethical issues because of the risks associated. [ABSTRACT FROM AUTHOR]

Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm. [ABSTRACT FROM AUTHOR]

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value. [ABSTRACT FROM AUTHOR]

Connectivity is an integral trait for swarm robotic systems to enable effective collaboration between the robots in the swarm. However, connectivity can be lost due to events that could not have been a priori accounted for. This paper presents a novel probabilistic connectivity-restoration strategy for swarms with limited communication capabilities. Namely, it is assumed that the swarm comprises a group of follower robots whose global connectivity to a base can only be achieved via a localized leader robot. In this context, the proposed strategy incrementally restores swarm connectivity by searching for the lost robots in regions-of-interest (RoIs) determined using probability theory. Once detected, newly found robots are either recruited to help the leader in the restoration process, or directly guided to their respective destinations through accurate localization and corrective motion commands. The proposed swarm-connectivity strategy, thus, comprises the following three stages: (i) identifying a discrete set of optimal RoIs, (ii) visitation of these RoIs, by the leader robot, via an optimal inter-region search path, and (iii) searching for lost robots within the individual RoIs via an optimal intra-region search path. The strategy is novel in its use of a probabilistic approach to guide the leader robot’s search as well as the potential recruitment of detected lost robots to help in the restoration process. The effectiveness of the proposed probabilistic swarm connectivity-restoration strategy is represented, herein, through a detailed simulated experiment. The significant efficiency of the strategy is also illustrated numerically via a comparison to a competing random-walk based method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The paradigm of multi-layer networks can help devise a set of robotic swarms interacting with mobile computing centrals. We present here a distributed hierarchical network model and a related routing protocol (based on static routing and/or AODV protocol for peer nodes) for swarm robotics in aquatic environment, defining also which packets need to be exchanged to guarantee the mission accomplishment. Joining concepts and techniques from different disciplines allows us building a robust system with potential practical applications in scenarios such as environmental care. We discuss our results and further developments of the proposed approach. [ABSTRACT FROM AUTHOR]

Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain‐specific artificial intelligence is more reliable, energy‐efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.

Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model's accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability. [ABSTRACT FROM AUTHOR]

In recent years, many microrobots have been developed for search applications using swarms in places where humans cannot enter, such as disaster sites. Hexapod robots are suitable for moving over uneven terrain. In order to use micro-hexapod robots for swarm exploration, it is necessary to reduce the robot's size while maintaining its rigidity. Herein, we propose a micro-hexapod with an SU-8 rigid frame that can be assembled from a single sheet. By applying the SU-8 coating as a structure to the hexapod and increasing the rigidity, the substrate size can be reduced to within 40 mm × 40 mm and the total length when assembled to approximately 30 mm. This enables the integration of the micro electromechanical systems (MEMS) process into small and inexpensive hexapod robots. In this study, we assembled the hexapod with a rigid frame from a sheet created using the MEMS process and evaluated the leg motion. [ABSTRACT FROM AUTHOR]

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively. [ABSTRACT FROM AUTHOR]

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm robotics in cluttered environments, but optimizing trajectories for each robot is still computationally expensive, with a computational complexity from O (k (n t , ε) n t 2) to O (k (n t , ε) n t 3) where n t is the number of parameters in the parameterized trajectory, ε is precision, and k (n t , ε) is the number of iterations with respect to n t and ε. Furthermore, the swarm is difficult to move as a group. To address this issue, we define and then construct the optimal virtual tube, which includes infinite optimal trajectories. Under certain conditions, any optimal trajectory in the optimal virtual tube can be expressed as a convex combination of a finite number of optimal trajectories, with a computational complexity of O (n t) . Afterward, a hierarchical approach including a planning method of the optimal virtual tube with minimizing energy and distributed model predictive control is proposed. In simulations and experiments, the proposed approach is validated and its effectiveness over other methods is demonstrated through comparison. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper shows the critical role of robotics and automation in waste management, presenting a broad analysis of their integration as a transformative step towards Industry 4.0. However, focusing on the challenges faced by growing cities in efficiently handling waste, the study emphasizes smart waste management solutions and the growing demand for innovative. Key components of Industry 4.0, including Artificial Intelligence (AI), Big Data, the Internet of Things (IoT) and Robotics, are explored for their potential to revolutionize waste management practices. The discussion involves the multidimensional impact of these technologies on waste process such as collection, sorting, and disposal processes. Examples such as the Pneumatic Waste Collection System 4.0 (PWC 4.0) and swarm robotics illustrate practical applications, highlighting their involvement to efficiency, sustainability, and inclusivity. By delving into the soft aspects of smart cities and the domains defined by Professor Dr. Rudolf Giffinger, the paper highlights the broader implications of Industry 4.0 in enhancing the quality of life for citizens. The integration of digital technologies into waste management processes aligns with the global agenda of sustainable development and environmental conservation, positioning it as a significant stride towards smarter and more environmentally conscious cities. [ABSTRACT FROM AUTHOR]

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots. [ABSTRACT FROM AUTHOR]

There is an established idea – found in science fiction, architectural studios, and scientific papers alike – of stainable buildings crafted from bio-based materials, colonized by plant and animal life, and blending seamlessly into the natural surroundings. Such buildings might one day be built, maintained and remodelled by swarms of autonomous robots, allowing them to evolve in response to the changing needs of their inhabitants. Inspired by that vision, this paper contributes to the field of swarm intelligence with a focus on robotic construction and human-swarm interaction. Along with a short literature review on robotic building, swarm intelligence and biocompatible building materials, the paper presents an open-source simulation of abstracted termite-like swarm construction. The focus is mainly on human-swarm interaction, specifically how to influence the emergent behaviour of an autonomous swarm in order to elicit a desired outcome while retaining the robustness and adaptability of a self-organized system. The simulator is used to demonstrate a set of four autonomous swarm behaviours that are representative of construction tasks.

Reliable navigation and localization systems are crucial for Autonomous Underwater Vehicles (AUVs). The limitations of electromagnetic signals underwater make the use of the Global Positioning System (GPS) impractical. As a result, real-time positioning often relies on expensive sensors like the Doppler Velocity Log (DVL) or acoustic ranging, integrated through sensor fusion techniques. This study investigates and compares three inverse dynamic models designed to estimate the velocity of AUVs using cost-effective Inertial Measurement Units (IMUs) and a model of thrust output based on Pulse Width Modulation (PWM) signals. These velocity estimates are then integrated with an Extended Kalman Filter (EKF) to determine the AUV’s state, and results are validated against ground truth data. Our findings demonstrate the feasibility and effectiveness of these models in providing a reliable and economical solution for low-cost AUV navigation, which is particularly interesting in swarm applications.

Unmanned vehicles have seen a significant increase in a wide variety of fields such as for logistics, agriculture and other commercial applications. Controlling swarms of unmanned vehicles is a challenging task that requires complex autonomous control systems. Reinforcement learning has been proposed as a solution to this challenge. We propose an approach based on agent masking to enable a simple Deep Q-Network algorithm to scale on large swarms while training on relatively smaller swarms. We train our approach using multiple swarm sizes and learning rates and compare our results using metrics such as the number of collisions. We also compare the ability of our approach to scale on swarms ranging from five to 25 agents using metrics and visual analysis. Our proposed solution was able to guide a swarm of up to 100 agents to a target while keeping a good swarm cohesion and avoiding collision.

In this paper, a decentralized consensus algorithm for a robotic swarm is presented, which enable agents to escape collisions and avoid obstacles collectively. To achieve consensus, we have used neighboring clusters and extreme function for data dissemination and consensus in agents to avoid obstacles respectively. The main challenge is to make fast and accurate collective decision by improving data propagation in presence of Byzantine agents. To improve data dissemination in local interactions of agents in decentralized fashion, an iterative rumor-based data propagation model is proposed. Due to presence of Byzantine robots, the LCP and the W-MSR algorithm cannot achieve consensus for obstacle avoidance in artificial swarms. We establish the Expectation-based Extreme Value (EEV) algorithm using the local expectation and the extreme function to solve these problems. The experiments conducted in simulations demonstrate that the rumor spreading method has better results than the Peer-to-Peer method in randomly connected swarm signaling network (SSN) with complex environmental circumstance, the EEV algorithm is more effective than the LCP and the W-MSR for the swarm navigation and consensus in agent on large obstacles / environmental features. Furthermore, in presence of malicious / hacked agents in a swarm it is very difficult to reach consensus. The result show that proposed algorithm can handle the Byzantine agents effectively.

Abstract 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.

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.

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization. [ABSTRACT FROM AUTHOR]

Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches. [ABSTRACT FROM AUTHOR]

Swarm robotics has emerged as a promising field with applications ranging from search and rescue operations to environmental monitoring. This paper introduces Anveshan, a 2-D swarm robot simulator developed to provide a user-friendly yet powerful platform for simulating and analyzing swarm behaviors. Focusing on the search use case, particularly in the context of Search and Rescue operations during disaster response, we showcase the capabilities of Anveshan in addressing the complexities associated with coordinated multi-robot systems. In this paper, we analyze the existing swarm simulators and define the architecture of Anveshan, along with details about all the included modules and configurable parameters offered by the simulator to demonstrate its flexibility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Swarm robotics is a distinctive type of multi-robotic system that relies on local communication among the swarm members to generate a desired global behaviour. This implies a lack of global information, requiring robots to sense and communicate using sensors and actuators located on their bodies. Consequently, the robots within the swarm must leverage collective intelligence to solve the problem at hand, as no individual robot can accomplish the task independently. This article provides an overview of swarm robotics in general, highlighting its characteristics that distinguish it from other multi-robotic systems and simultaneously serve as motivation to adopt a swarm robotics approach. A closer examination of collective decision making within swarm robotics and its design problem also provided, classifying design methods into manual design and automatic approaches. The most commonly used automatic approaches to design collective decision making in swarm robotics are explained, along with a mention of the benefits and drawbacks of such approaches. However, this review does not cover aspects such as the swarm collective behaviours – except collective decision making – and the swarm robotics tasks. [ABSTRACT FROM AUTHOR]

We study the large time behavior of a system of interacting agents modeling the relaxation of a large swarm of robots, whose task is to uniformly cover a portion of the domain by communicating with each other in terms of their distance. To this end, we generalize a related result for a Fokker-Planck-type model with a nonlocal discontinuous drift and constant diffusion, recently introduced by three of the authors, of which the steady distribution is explicitly computable. For this new nonlocal Fokker-Planck equation, existence, uniqueness and positivity of a global solution are proven, together with precise equilibration rates of the solution towards its quasi-stationary distribution. Numerical experiments are designed to verify the theoretical findings and explore possible extensions to more complex scenarios. [ABSTRACT FROM AUTHOR]

There is an established idea – found in science fiction, architectural studios, and scientific papers alike – of stainable buildings crafted from bio-based materials, colonized by plant and animal life, and blending seamlessly into the natural surroundings. Such buildings might one day be built, maintained and remodelled by swarms of autonomous robots, allowing them to evolve in response to the changing needs of their inhabitants. Inspired by that vision, this paper contributes to the field of swarm intelligence with a focus on robotic construction and human-swarm interaction. Along with a short literature review on robotic building, swarm intelligence and biocompatible building materials, the paper presents an open-source simulation of abstracted termite-like swarm construction. The focus is mainly on human-swarm interaction, specifically how to influence the emergent behaviour of an autonomous swarm in order to elicit a desired outcome while retaining the robustness and adaptability of a self-organized system. The simulator is used to demonstrate a set of four autonomous swarm behaviours that are representative of construction tasks. [ABSTRACT FROM AUTHOR]

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints. [ABSTRACT FROM AUTHOR]

This paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called exhibited decision, determines the agent's state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems. [ABSTRACT FROM AUTHOR]

The Cerrado biome in Brazil plays a vital role in preserving biodiversity, providing essential ecosystem services, and supporting agriculture, making it a crucial and valuable natural resource. Sete Cidades National Park stands out for its rock formations, 10,000-year-old cave paintings and its Cerrado vegetation. The Cerrado is known for being a pyrobiome, so its patrolling becomes essential. In this context, this paper introduces a novel approach to swarm robotics patrolling in the unique ecosystem of the Sete Cidades National Park, located within the Cerrado biome. The study presents three distinct cellular automata models designed for the task, aiming to enhance the efficiency and coverage of patrolling efforts. The key difference between our model presented in this research and the previous one is our focus on a map that encompasses diverse vegetation types, specifically designed to represent the Sete Cidades National Park, with a primary goal of monitoring forest fires. The results demonstrated that the best-performing model was the Forest Tabu Inverted Ant Cellular Automata, which achieved an average of 21.77 complete patrol cycles with 95% confidence. This outcome was obtained using three robots, a tabu queue of |Q| = 80, and a maximum pheromone per cell equals to ρ = 103. These parameters highlight the efficacy of this model in optimizing patrol cycles and the efficient use of resources for environmental surveillance in the Sete Cidades National Park, particularly in the context of fire prevention.

In rational decision-making processes, the information interaction among individual robots is a critical factor influencing system stability. We establish a game-theoretic model based on mutual information to address division of labor decision-making and stability issues arising from differential information interaction among swarm robots. Firstly, a mutual information model is employed to measure the information interaction among robots and analyze its influence on the behavior of individual robots. Secondly, employing the Cournot model and the Stackelberg model, we model the diverse decision-making behaviors of swarm robots influenced by discrepancies in mutual information. The intricate decision dynamics exhibited by the system under the disparity mutual information values during the game process, along with the stability of Nash equilibrium points, are analyzed. Finally, dynamic complexity simulations of the game models are simulated under the disparity mutual information values: (1) When ν1 of the game model varies within a certain range, the Nash equilibrium point loses stability and enters a chaotic state. (2) As I(X;Y) increases, the decision-making pattern of robots transitions gradually from the Cournot game to the Stackelberg game. Concurrently, the sensitivity of swarm robotics systems to changes in decision parameter decreases, reducing the likelihood of the system entering a chaotic state.