Michail‐Alexandros Kourtis, George Xilouris, Michael C. Batistatos, Anastasios Kourtis · 5 authors
Abstract—Reliable and ubiquitous communications, offering high data rates, low latency and supporting large numbers of connected devices, are critical requirements for modern emergency rescue missions. Multiple teams of First Responders, operating at remote areas, on rough terrain or under harsh conditions (e.g. wildfires, earthquakes, flooding etc.) need seamless connectivity to send/receive mission data and organize their operations. Decentralized swarm computing architectures offer a wide range of capabilities to enhance and accelerate edge processing for critical use case scenarios. This paper presents a converged approach on swarm computing and intelligence using Decentralized Autonomous Organizations for emergency communications, and how a swarm of drones can leverage different edge accelerators for different applications.
Abstract Lately, the importance of swarm robotics has been recognized in a wide range of areas, including logistics, surveillance, disaster management, agriculture, and other industrial applications. The swarm intelligence introduced by the existing paradigm of artificial intelligence and machine learning often ignores the aspect of providing security and reliability guarantees. Consider a futuristic scenario wherein self-driving cars will transport people, self-driving trucks will carry cargo between warehouses, and a combination of legged robots/drones will ship cargo from warehouses to doorsteps. In the case of such a heterogeneous swarm of robots, it is crucial to ensure a trustful and reliable operating platform for smooth coordination, collaborative decision-making via appropriate consensus, and seamless information sharing while ensuring data security. In this direction, blockchain has been proven to be an effective technology that maintains the transactions (records) in a trustful manner after being validated through consensus. This guarantees accountability, transparency, and trust concerning the storage, safeguarding, and sharing of information among the parties. In this paper, we provide a walkthrough demonstrating the feasibility of using blockchain technology to make the robotic swarm trustful systems in their adoption to critical applications at large-scale. We highlight the pros and cons of the use of cloud vis-a-vis blockchain in swarm robotics. Finally, we present various future research opportunities pertaining to the adoption of blockchain technology in swarm robotics applications.
Sanjaya Mallikarachchi, Can Dai, Oshani Seneviratne, Isuru S. Godage
The growing number of applications in Cyber-Physical Systems (CPS) involving different types of robots while maintaining interoperability and trust is an ongoing challenge faced by traditional centralized systems. This paper presents what is, to the best of our knowledge, the first integration of the Robotic Operating System (ROS) with the Ethereum blockchain using physical robots. We implement a specialized smart contract framework called “Swarm Contracts” that rely on blockchain technology in real-world applications for robotic agents with human interaction to perform collaborative tasks while ensuring trust by motivating the agents with incentives using a token economy with a self-governing structure. The use of open-source technologies, including robot hardware platforms such as TurtleBot3, Universal Robot arm, and ROS, enables the ability to connect a wide range of robot types to the framework we propose. Going beyond simulations, we demonstrate the robustness of the proposed system in real-world conditions with actual hardware robots.
The Delegated Proof of Stake (DPoS) consensus mechanism uses the power of stakeholders to not only vote in a fair and democratic way to solve a consensus problem, but also reduce resource waste to a certain extent. However, the fixed number of member nodes and single voting type will affect the security of the whole system. In order to reduce the negative impact of the above problems, a new consensus algorithm based on vague set and node impact factors is proposed. We first use fuzzy values to calculate the ratings of all nodes and initially determine the number of agent nodes according to the preset threshold value. Then, we judge whether a secondary screening is needed. If needed, calculating the nodes' impact factor based on their neighboring nodes, and combining their impact factors with adjacency votes to further distinguish the nodes with the same fuzzy value. In addition, we analyze the dynamic changes in the composition and scale of the agent node set and give its ideal size through testing. Finally, we compare the proposed algorithm with DPoS algorithm and existing fuzzy set-based algorithms in different scales and network structures. Results show that no matter in what kind of network structures, the effectiveness of the proposed algorithm is improved. Among which, the most noticeable improvement is seen in complex network structures.
Simultaneous localization and mapping (SLAM) in unmanned vehicles, such as drones, has great usability potential in versatile applications. When operating SLAM in multi-drone scenarios, collecting and sharing the map data and deriving converged maps are major issues (regarded as the bottleneck of the system). This paper presents a novel approach that utilizes the concepts of distributed ledger technology (DLT) for enabling the online map convergence of multiple drones without a centralized station. As DLT allows each agent to secure a collective database of valid transactions, DLT-powered SLAM can let each drone secure global 3D map data and utilize these data for navigation. However, block-based DLT—a so called blockchain—may not fit well to the multi-drone SLAM due to the restricted data structure, discrete consensus, and high power consumption. Thus, we designed a multi-drone SLAM system that constructs a DAG-based map database and sifts the noisy 3D points based on the DLT philosophy, named DAGmap. Considering the differences between currency transactions and data constructions, we designed a new strategy for data organization, validation, and a consensus framework under the philosophy of DAG-based DLT. We carried out a numerical analysis of the proposed system with an off-the-shelf camera and drones.
This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network.
Internet of Things (IoT) can be conveniently de-ployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the IoT data collection with blockchain-based security provisioning, towards efficient and safeguarded IoT operations. In particular, a blockchain with proof-of-stake (PoS) consensus mechanism is constructed among the UAVs with the collected IoT data. Correspondingly, we optimize the IoT communication and the UAV deployment for the maximum blockchain throughput considering the PoS procedure. The problem is solved with a deep deterministic policy gradient-based approach, where the power allocation is obtained with closed-form solutions and the UAV deployment is learned with actor-critic networks. Simulation results are provided to show the deployment and performance, corroborating the effectiveness of our proposal.
Kailei Tang, Zhiyan Dong, Tianlun Dai, Zhongxue Gan
The issues of managing swarm intelligence are essential to many multiple tasks. The scenarios are complex and dynamic, which is hard for a single agent to satisfy the needs of various tasks. As thus, a practical intelligence cooperative combat scheme, composed of multiple agents, is required to supply a effective and efficient consensus for swarm intelligence with external conditions evolving. Among this scheme, the accuracy of information sharing, transmission, and the integrity of the stored data are all critically significant. The Blockchain, a digital distributed ledger,is distributed on different nodes, and each node holds the same data, which attracts increasing attentions recently. There are many consensus algorithms which is the key part of the blockchain, such as PoW, PoS, DPoS, PoR, etc. However, no matter in terms of performance, security and stability, existing work can hardly support a battle plan oriented to swarm intelligence. To our knowledge, there is no consensus algorithm that takes into account the resources of agent in swarm intelligence collaboration. Therefore, we introduce an optimization of DPoS for swarm intelligence based on agent behavior monitoring and agent’s own resource analysis (Delegated Proof of Stake based node’s Behavior and Resource, DPoSBR). Combining the situation of malicious behaviors of the agent and the resources of the agent, we choose the more trustworthy agent as the captain. Therefore, the captain agent is more secure and the election process is fairer. Finally, the extensive simulations are conducted to evaluate the performance of DPoSBR algorithm, which has good practicability. Meanwhile, it enables more agents to participate, which is beneficial to the decentralization of the system and can promote the enthusiasm of the entire agents, and it prevents the malicious agents from doing malicious behaviors again.
Eduardo Castelló Ferrer, Ernesto Jiménez, José Luis López-Presa, Javier Martín-Rueda
Interest in multirobot systems is rising rapidly both in academia and in industry. The use of multiple robots working in a coordinated way, rather than a single robot, has several advantages in a diverse range of applications. However, few studies have focused on operations in which robots can behave maliciously and alter the outcome of the collective mission. In this article, we present a set of Byzantine Follow The Leader (BFTL) problems, in which a subset of robots in the system shows unintended or inconsistent behavior (i.e., Byzantine robots). In the BFTL problems,leadersdiscover routes from their starting positions to specific destinations and guide thefollowers, whileByzantinerobots try to hinder the leaders and mislead the followers. In this research, blockchain technology is used as a communication tool within multirobot systems, forleadersto broadcast directions to the whole group. We propose algorithms to tackle the BFTL problems, prove their correctness, and validate them in simulated experiments of realistic scenarios. Results show that the proposed algorithms mitigate the impact of Byzantine robots in multirobot systems conducting a BFTL mission. Our analysis provides minimum and maximum boundary calculations for important metrics including number of robots reaching their destination, number of steps taken, and weight requirements of the chain used during the mission. Our results provide a path toward the deployment of byzantine-resistant real-world multirobot systems.
In this paper, two blockchain-based consensus mechanisms are proposed for distributed control systems, those are the proof-of-work-based (PoW-based) and proof-of-stake-based (PoS-based) consensus mechanisms. The basic idea and process of these blockchain-based consensus mechanisms are introduced, giving rise to the dynamic, impartial and efficient consensus compared with the consensus protocol achieved by traditional control theories.
Mário Gabriel Santos De Campos, Caroline Ponzoni Carvalho Chanel, Corentin Chauffaut, Jérôme Lacan
This study describes a blockchain-based multi-unmanned aerial vehicle (multi-UAV) surveillance framework that enables UAV coordination and financial exchange between system users. The objective of the system is to allow a set of Points-Of-Interest (POI) to be surveyed by a set of autonomous UAVs that cooperate to minimize the time between successive visits while exhibiting unpredictable behavior to prevent external agents from learning their movements. The system can be seen as a marketplace where the UAVs are the service providers and the POIs are the service seekers. This concept is based on a blockchain embedded on the UAVs and on some nodes on the ground, which has two main functionalities. The first one is to plan the route of each UAV through an efficient and computationally cheap game-theoretic decision algorithm implemented into a smart contract. The second one is to allow financial transactions between the system and its users, where the POIs subscribe to surveillance services by buying tokens. Conversely, the system pays the UAVs in tokens for the provided services. The first benchmarking experiments show that the IOTA blockchain is a potential blockchain candidate to be integrated in the UAV embedded system and that the chosen decentralized decision-making coordination strategy is efficient enough to fill the mission requirements while being computationally light.
Vyacheslav Petrenko, Фариза Тебуева, Sergey Ryabtsev, Igor Struchkov
Abstract The technological development and popularity of swarm robotics actualizes the issues of increasing the efficiency and safety of consensus achievement between the swarm elements. Currently, most studies have little or no regard for information security issues when consensus achievement among swarm elements in the presence of robots with arbitrarily malfunctioning or malicious behavior. However, in many practical applications, when exploring the external environment, a swarm of one or more malicious robots may be sufficient to allow the current consensus mechanisms to fail. One of the promising ways to ensure information security in collective decision-making is the use of blockchain technology as a distributed system designed to work in conditions of lack of trust between the parties. The use of distributed ledger technologies, due to their complexity, leads to reducing efficiency in consensus achievement. The aim of the work is to improve the safety of the process of consensus achievement in swarms of robots through the use of blockchain technology in collective design making while maintaining the effectiveness of consensus achievement between robots. Increasing the safety of decision-making by a swarm will increase the stability and the possibilities of practical application of swarm robotic systems for solving problems in aggressive environments.
The active introduction of robotics swarm systems into life brings the issues of their information security up to date. Known security approaches often do not take into account the peculiarities of the implementation of swarm systems, such as collective design-making, and only consider the presence of Byzantine robots with a strategy of behavior consisting in voting against a majority when a consensus is reached. The aim of this work is to increase the security of the collective design-making process in a swarm robotics system from the imposition of false and ineffective alternatives by Byzantine robots with a random behavior strategy. It is proposed to use an approach based on the use of a distributed ledger and analysis of deviations in the process of collective design-making, which will allow identifying and isolating harmful effects. The solution to the problem of detecting Byzantine robots is based on the application of the criterion of the degree of confidence of a robot in choosing an alternative when a consensus is reached by the swarm system and is based on the assumption that the distribution of the degree of confidence of a Byzantine robot due to ignoring the parameters of the external environment and voting for random alternatives is significantly different from the behavior an ordinary robot. The elements of novelty of the presented solution include the use of the degree of confidence criterion to ensure the safety of collective design-making and the ability to take into account various strategies of behavior of Byzantine robots. The use of the presented solution makes it possible to increase the efficiency of reaching consensus by a swarm robotics system in the presence of Byzantine robots. The simulation for a swarm of 20 robots, including 5 Byzantine ones with random behavior, showed an increase in the probability of correctly reaching a consensus by 12.5%. The practical significance of the presented solutions lies in the possibility of ensuring the stability of reaching consensus by a swarm robotics system in the presence of robots with harmful behavior.
Animals team up to collectively address challenges they could not overcome individually. Several species self-organize into large groups to leverage vital behaviors such as foraging, construction, or predator evasion. Ants, for instance, find shortest paths to food resources by depositing pheromones, bees indicate direction and distance to flower meadows through waggle dances in the hive, and fish display evasive maneuvers to escape predators. These three examples illustrate a collective problem-solving ability that leverages the cognition and actions of individually limited organisms. With the advancement of robotics and automation, engineered multi-agent systems have been inspired to achieve similarly high degrees of scalable, robust, and adaptable autonomy through decentralized and dynamic coordination. Scientists have demonstrated ground-based collective transport, construction, and self-assembly, in some cases with several hundred robots. Multiple aerial swarms fly complex maneuvers, some of them even with little external assistance. Small robot teams, although with limited autonomy, have been engaged to assist in search and rescue missions at sea, sample oceanic data, and find unknown deep-sea species. Overall however, robot swarms have been most successfully demonstrated in two-dimensional (2D) space or with partial assistance from central controllers and external tracking. In addition, many more demonstrations of self-organized collectives exist above-ground as opposed to the less explored underwater domain, which is particularly challenging because it often precludes traditional communication methods such as radio and GPS signals. Few underwater swarms exist and achieve limited coordination complexity and scale because they rely on explicit message passing. In this dissertation, I introduce a novel underwater robot collective, the Blueswarm, which realizes full 3D spatiotemporal coordination without any external assistance. Each Bluebot is equipped with four independently controllable fins and two wide-angle lens cameras for 3D locomotion and perception. The vision system is complemented by three LEDs, which encode information about direction, distance and heading, and facilitate implicit coordination among robots. In the bioinspired design process, I pursued simplicity in both hardware and software to enable real-time onboard multi-robot tracking for local decision making followed by swift action. Blueswarm is the first 3D underwater collective that uses only local implicit vision-based coordination to self-organize. Inspired by the dynamic and agile coordination of fish, I show that complex and dynamic 3D collective behaviors — synchrony, aggregation-dispersion, dynamic circle formation, search-capture, and escape — can be achieved by sensing minimal, noisy impressions of neighbors without any centralized intervention. To the best of my knowledge, this is the first significant demonstration of unsupervised and autonomous 3D collective coordination underwater. Accompanied by a custom simulator, the Blueswarm platform gives researchers a much-needed tool to systematically develop and test algorithms for self-organzied 3D collective behaviors in the laboratory. The results of this dissertation provide insights into the power of implicit coordination and advance the potential for future underwater robots that display collective capabilities on par with fish schools for applications such as environmental monitoring and search in coral reefs and coastal environments. In addition, the Bluebots are also well suited as an experimental testbed for investigating natural collective behaviors and biomimicry, for example, studying the energy savings for different formations in schooling fish or the performance landscape of aquatic propulsion with a diverse set of caudal fins.
Jonathan Grey, Isuru S. Godage, Oshani Seneviratne
Multi-agent robotic systems are becoming pervasive in many real-world applications from search and rescue missions to future household robotic appliances that might need to work together to achieve specific tasks. We propose and implement a collaborative environment for secure communication of robotic agents in a prototype agent system that mimics the interactions between agents of varying behaviors using special-purpose smart contracts titled "Swarm Contracts." This paper describes how Swarm Contracts and blockchain technologies increase the interaction efficacy between agents by providing a more trusted information exchange to reach consensus under trustless conditions, assess agent productivity, allocate plans and tasks to deploy distributed solutions, and carry out joint missions. All these features are encapsulated in Swarm Contracts, making the decentralized applications that use them a viable alternative to centralized command and control applications that are pervasive in multi-agent robotics applications of today. We have evaluated the utility of the developed Swarm Contracts in adversarial settings and report the results that are very promising for future applications of such decentralized heterogeneous robotic agent interactions.
Achieving efficient and reliable self-organization in groups of autonomous robots is a fundamental challenge in swarm robotics. Even simple states of collective motion, such as group translation or rotation, require nontrivial algorithms, sensors, and actuators to be achieved in real-world scenarios. We study here the capabilities and limitations in controlling experimental robot swarms of a decentralized control algorithm that only requires information on the positions of neighboring agents, and not on their headings. Using swarms of e-Puck robots, we implement this algorithm in experiments and show its ability to converge to self-organized collective translation or rotation, starting from a state with random orientations. Through a simple analytical calculation, we also unveil an essential limitation of the algorithm that produces small persistent oscillations of the aligned state, related to its marginal stability. By comparing predictions and measurements, we compute the experimental noise distributions of the linear and angular robot speeds, showing that they are well described by Gaussian functions. We then implement simulations that model this noise by adding Gaussian random variables with the experimentally measured standard deviations. These simulations are performed for multiple parameter combinations and compared to experiments, showing that they provide good predictions for the expected speed and robustness of the self-organizing dynamics.
Volker Strobel, Eduardo Castelló Ferrer, Marco Dorigo
Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as "meta-controllers" and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.
Nowadays, urban environments suffer from recurrent traffic jams and their associated side effects. In order to cope with this issue, several solutions were proposed, mostly relying on road-operated traffic management strategies. Recent studies show the potential of connected and automated cars to improve traffic by adapting individual behaviors. For instance, thanks to autonomous decision-making capabilities, platoon systems enable a group of vehicles to travel attached to each other by a virtual link. Multi-Agent Systems (MAS) empowered by Cyber Physical Systems (CPS) can be used to control a fleet of cars in a decentralized way. This paper applies this concept for dynamic platoon vehicles system through a multi-agent model based on Newtonian physics to achieve a self-organization at the. The behavior can be improved by offering more computing power or a better knowledge of the environment thanks to a micro-service division. In this proposal, the vehicle is considered to be a physical agent with personal behavior divided into three layers : perception based on sensor, decision based on physics simulation and application helped by embedded robotics.