Saiprasanth Krishnamoorthy, Albert P. Go, Ashlee Tiwari, Vikram Kapila
This paper develops a distributed technique to populate the network graph of a decentralized multi-robot system (MRS) by employing a consensus protocol for extracting the identities and states of each robot's neighbors in the MRS. A dark-room exchange (DRE) technique is proposed wherein each robot uses its on-board 2D LiDAR for range sensing and peer-to-peer communication to identify and track neighboring objects. The resulting information is utilized to build and maintain a distributed ledger populated with the information of the MRS network graph structure to facilitate supervision by human operators. The system is tested in a simulated environment consisting of TurtleBot3 robots scattered in a 2D plane. Using the results of simulation, an analysis of the speed and performance of the DRE technique is conducted that illustrates high reliability and fast response times. The paper concludes with a discussion of the future scope of this research for multi-robot/swarm applications.
Abstract The paper considers the problem of distributed decision making in the robot swarm. The enhancement technique of the related study approach is proposed using the data transmission distance constraints and the weighted voting strategy. The decision making process is organized by means of distributed ledger usage. The information propagation through the swarm is implemented via spreading randomized rumor. The avoidance of routing in the swarm improves the overall energy efficiency of the system. The weighted voting transactions take into account the positions of the robots relating to the unknown objects or obstacles, as well as the voting history, which is stored in a distributed ledger
Each of the most popular blockchain platform, Bitcoin, Ethereum, and Hyperledger Fabric, varies in aspects of decentralization, permission, anonymity, and native-currency, has its own consensus mechanism, algorithm and implementation. In the mainstream blockchain technology, there are many common consensus algorithms. They differ in terms of computational complexity, fault-tolerance, and resilience. The performance, consistency, scalability, and efficiency of blockchain consensus mechanism need further improvement and optimization. Consensus mechanism and code implementation of Bitcoin, Ethereum and Hyperledger are analyzed, discussed and proposed.
Isaac J. Jensen, Daisy Flora Selvaraj, Prakash Ranganathan
Unmanned Aerial Vehicle (UAV) technology is quickly growing with a wide range of current and planned future applications. As the technology grows in usage, the data gathered by UAV systems as well as the UAVs themselves will become bigger targets for cyber-attacks. New cyber security technologies, such as the immutable ledger technology known as blockchain, should therefore be applied to provide a defense against the growing threat of cyber-attacks. This paper explores blockchain technology, first through a general overview of its components and characteristics, and then at what security improvements it can provide to a system. Following this exploration, the application of blockchain to a UAV swarm environment is briefly expanded on. Lastly, one such blockchain framework known as Hyperledger Fabric is explored, that could potentially be applied to a swarm of UAVs to increase its security.
This article discusses the theoretical composition of the two consensus algorithms in order to obtain a fundamentally new solution to the problem of consensus in a distributed ledger technologies.
Alex Khawalid, Dan Acristinii, Hans van Toor, Eduardo Castelló Ferrer
Swarm Robotics (SR) faces a series of challenges impeding widespread adoption for real-world applications. Distributed Ledger Technology (DLT) has shown it can solve a number of these challenges. An experiment was conducted to showcase the resolution of these challenges. A search and rescue mission was simulated using drones coupled with single board computers and several simulated agents. Inter-agent communications were facilitated through DLT in a completely decentralized network. A frontend interface was built to demonstrate the ease with which information can be extracted from the system. This paper shows the feasibility of the application of DLT to SR-related challenges in a practical experiment. For future work, it is proposed to focus on more complex tasks through federated learning or inter-swarm communications, possibly through Cosmos.
Blockchain is a disruptive technology that is normally used within financial applications, however it can be very beneficial also in certain robotic contexts, such as when an immutable register of events is required. Among the several properties of Blockchain that can be useful within robotic environments, we find not just immutability but also decentralization of the data, irreversibility, accessibility and non-repudiation. In this paper, we propose an architecture that uses blockchain as a ledger and smart-contract technology for robotic control by using external parties, Oracles, to process data. We show how to register events in a secure way, how it is possible to use smart-contracts to control robots and how to interface with external Artificial Intelligence algorithms for image analysis. The proposed architecture is modular and can be used in multiple contexts such as in manufacturing, network control, robot control, and others, since it is easy to integrate, adapt, maintain and extend to new domains.
DI-fusion, le Dépôt institutionnel numérique de l'ULB, est l'outil de référencementde la production scientifique de l'ULB.L'interface de recherche DI-fusion permet de consulter les publications des chercheurs de l'ULB et les thèses qui y ont été défendues.
In recent years, numerous studies have focused on the mathematical modeling of social dynamics, with self-organization, i.e., the autonomous pattern formation, as the main driving concept. Usually, first or second order models are employed to reproduce, at least qualitatively, certain global patterns (such as bird flocking, milling schools of fish or queue formations in pedestrian flows, just to mention a few). It is, however, common experience that self-organization does not always spontaneously occur in a society. In this review chapter we aim to describe the limitations of decentralized controls in restoring certain desired configurations and to address the question of whether it is possible to externally and parsimoniously influence the dynamics to reach a given outcome. More specifically, we address the issue of finding the sparsest control strategy for finite agent-based models in order to lead the dynamics optimally towards a desired pattern.
Open access
2 source records
Opinion Dynamics and Social Influence
Mathematical and Theoretical Epidemiology and Ecology Models
Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.
Modern ocean exploration and sensing approaches have been mainly based on Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and/or static Underwater Acoustic Sensor Networks (UASNs) deployments. Individual AUVs and ROVs represent a single point of failure in addition to being bulky and expensive as vehicles are usually full-featured and sophisticated. UASNs have traditionally been statically deployed. This limits their use to original deployment locations and renders them unsuitable for search tasks. Swarm Robotics (SR) are a natural, better alternative. Swarms possess superior features over a sophisticated AUV; they are smaller, cheaper, robust, reliable, and scalable by design and definition. They also have the sensing capabilities of UASNs and built-in active mobility.\nDesigning successful swarm missions in harsh aquatic environments is an involved task. We address this by analyzing the indispensable stages of a typical mission and carefully designing decentralized algorithms to achieve the desired per-stage goals. Important system and environmental parameters are taken into consideration to achieve the end goal: completing mission requirements while respecting time constraint, with best possible performance and minimum loss of agents. Special attention is given to target search, task identification and allocation, and mission-stage integration due to their importance. Identifying target location in an unbounded environment is challenging. Bandwidth limited and intermittent communication complicates the process further. Therefore, we develop global search algorithms that use minimal communication and utilize flocking to maintain cohesion. These algorithms have multiple advantages over traditional ones in terms of convergence time, omni-directionality, consideration of physical constraints, and being self-bounding. At the target, tasks are autonomously identified and allocated in a completely decentralized manner. Validation of the developed techniques is done through realistic simulations and analytical comparisons.\nOur main contributions are: 1) a general framework for underwater mission planning, 2) three novel global search algorithms for unbounded underwater environment, 3) an algorithm for initial self-organization, 4) an optimized same-position reorientation algorithm for use in certain mission stages, 5) three autonomous task allocation algorithms, 6) three local target search algorithms, 7) a measure of mission utility, and 8) the design of a human brain-inspired model to support learning and complete autonomy.
Many tasks requiring multiple autonomous underwater vehicles (AUVs) are simple, with static goals, of short duration, and require few AUVs, often of the same type. Simple coordination mechanisms that assign roles to AUVs before the mission are sufficient for these multi-AUV systems. However, for tasks that are complex and dynamic, of long duration (implying that AUVs will come and go during the mission), and that have many heterogeneous AUVs, organization of the system will not work. In addition, due to changes in the situation, the system will likely need to be reorganized during the mission. We are developing a distributed, context-aware self-organization/reorganization scheme for advanced multi-AUV systems. This is a two-level approach in which a meta-level organization first self-organizes, assesses the context, and uses contextual knowledge to design a task-level organization appropriate for the context that can then carry out the mission. We are extending our prior work by distributing both the context assessment process and the organization design process. The result will be a system that can self-organize efficiently and effectively for its context and that can reorganize appropriately as the context changes.
Tomassino Ferrauto, Domenico Parisi, Gabriele Di Stefano, Gianluca Baldassarre
Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.
Purpose This paper aims to present an approach for a bio‐inspired decentralization topology control mechanism, called force‐based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each holonomic autonomous vehicle (HAV) in a mobile ad hoc network (MANET) as software agent to achieve a uniform spread of HAVs and to provide a fully connected network over an unknown geographical terrain. An HAV runs its own FGA to decide its next movement direction and speed based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. Design/methodology/approach The objective function used in FGA is inspired by the equilibrium of the molecules in physics where each molecule tries to be in the balanced position to spend minimum energy to maintain its position. In this approach, a virtual force is assumed to be applied by the neighboring HAVs to a given HAV. At equilibrium, the aggregate virtual force applied to an HAV by its neighbors should sum up to zero. If the aggregate virtual force is not zero, it is used as a fitness value for the HAV. The value of this virtual force depends on the number of neighbors within the communication range of R com and the distance among them. Each chromosome in our GA‐based framework is composed of speed and movement direction. The FGA is independently run by each HAV as a topology control mechanism and only utilizes information from neighbors and local terrain to make movement and speed decisions to converge towards a uniform distribution of HAVs. The authors developed an analytical model, simulation software and several testbeds to study the convergence properties of the FGA. Findings The paper finds that coverage‐centric, bio‐inspired, mobile node deployment algorithm ensures effective sensing coverage for each mobile node after initial deployment. The FGA is also an energy‐aware self‐organization framework since it reduces energy consumption by eliminating unnecessary excessive movements. Fault‐tolerance is another important feature of the GA‐based approach since the FGA is resilient to losses and malfunctions of HAVs. Furthermore, the analytical results show that the authors' bio‐inspired approach is effective in terms of convergence speed and area coverage uniformity. As seen from the experimental results, the FGA delivers promising results for uniform autonomous mobile node distribution over an unknown geographical terrain. Originality/value The proposed decentralized and bio‐inspired approach for autonomous mobile nodes can be used as a real‐time topology control mechanism for commercial and military applications since it adapts to local environment rapidly but does not require global network knowledge.
In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.
We address a self-organizing network problem for autonomous robotic sensor swarms towards various ad hoc sensor network applications. For this purpose, a decentralized solution approach is proposed to enable individual robots to build and to maintain their network through locally communicative interactions with adjacent robots. Specifically, the communicative interaction allows robots to select specific neighbors with higher connectivity, and to adapt to network topological changes by robot movements. The proposed approach is verified to be effective through extensive simulations for the secure self-organization of a robotic sensor network. Considering the realistic conditions of communication delays and re-configuration for robot disappearances due to robot failures, simulations are also performed.
This paper presents a selective survey of theoretical and experimental progress in the development of biologicallyinspired approaches for complex surveillance and reconnaissance problems with multiple, heterogeneous autonomous systems. The focus is on approaches that may address ISR problems that can quickly become mathematically intractable or otherwise impractical to implement using traditional optimization techniques as the size and complexity of the problem is increased. These problems require dealing with complex spatiotemporal objectives and constraints at a variety of levels from motion planning to task allocation. There is also a need to ensure solutions are reliable and robust to uncertainty and communications limitations. First, the paper will provide a short introduction to the current state of relevant biological research as relates to collective animal behavior. Second, the paper will describe research on largely decentralized, reactive, or swarm approaches that have been inspired by biological phenomena such as schools of fish, flocks of birds, ant colonies, and insect swarms. Next, the paper will discuss approaches towards more complex organizational and cooperative mechanisms in team and coalition behaviors in order to provide mission coverage of large, complex areas. Relevant team behavior may be derived from recent advances in understanding of the social and cooperative behaviors used for collaboration by tens of animals with higher-level cognitive abilities such as mammals and birds. Finally, the paper will briefly discuss challenges involved in user interaction with these types of systems.
Purpose The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found. Design/methodology/approach In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network. Findings Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks. Originality/value The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.