Co-Designing Programmable Fidgeting Experience with Swarm Robots for Adults with ADHD

The actual problem of studying military logistics actions to form missions of a swarm of attack drones for conducting massive attacks on enemy targets is formed and solved. The research is aimed at planning military attacks with drones to provide for establishing military parity of forces in a military conflict zone. Therefore, the actual topic of the proposed publication, which explores the sequence of military logistics actions for planning and implementing wave attacks to inflict maximum damage on the enemy, is relevant. The goal of this publication is to create a set of mathematical, simulation and agent-based models that can be used to study and plan attack missions by waves of swarms of attack drones. This article analyzes the existing problems of using a new innovative technological tool of warfare in the form of a swarm of attack drones. A systematic analysis of military logistics for conducting massive attacks with strike drones is carried out. A set of strategies for using a swarm of drones on the battlefield is formed. Possible actions related to launching drones, forming a swarm in flight, and dividing the swarm into groups with their movement to separate enemy targets are considered. Risks of enemy military threats (use of electronic warfare, anti-drone warfare, etc.) that affect the formation of routes and the possible destruction of a portion of attack drones on approach to targets are taken into account. The swarm is rationally divided into groups in flight, considering the combat potential required to defeat the enemy’s current targets. A sequence of drone swarm waves is formed depending on the level of target damage (partial damage, complete damage). When planning each wave of the drone flight, one of the proposed strategies is used. Much attention is paid to the formation of flight routes for attack drones to ensure the suddenness of a massive attack despite enemy military threats. An algorithm for generating the shortest flight route in time despite enemy anti-drone operations has been developed. A map of the air situation on the battlefield with a set of separate local zones that have different values of threat risks is formed. The route is planned considering the military risks of each local zone. A simulation model was created to study the movement of attack drones to enemy targets in time. An agent-based model is being developed to plan military logistics actions for conducting wave attacks on enemy targets using swarms and groups of attack drones. An illustrated example of planning the flight routes of a swarm of attack drones is presented, taking into account the risks of military threats. The scientific innovation of the study is related to the solution of the actual scientific and applied problem of planning the missions of a swarm of attack drones to ensure successful operational and tactical actions on the battlefield. The results of the research should be used by the military to plan and conduct attacks on enemy targets in the form of waves of swarms of attack drones.

Swarm robotics is an emerging research area combining swarm intelligence and robotics. Thanks to the recent achievements in optimization problem using swarm intelligence, searching problems in swarm robotics have attracted a large number of researchers. In searching problems, a swarm of robots searches for multiple targets in the environment without knowing any prior knowledge about the targets. This progress is quite similar with that of optimization problems in many aspects. Moreover, in most of the swarm robotics searching problems so far, some kinds of fitness functions are introduced for guiding the search of the swarm. This makes it a natural advantage to introduce swarm intelligence algorithms into swarm robotics. In this chapter, inspired by the fireworks algorithm, the group explosion strategy (GES) is proposed for searching multiple targets in swarm robotics. In the GES model, the whole swarm is divided into several groups. Robots in a group are spatially adjacent within the sensing range of each other. The swarm searches and collects targets in the environment without prior knowledge. Different groups do not intersect directly and their search for targets is parallel and independent. Through certain strategies, groups that run into each other will be re-arranged into new groups with possibly different members and search directions. In this way, inter-group cooperation can emerge in the swarm. The simulation results indicate that the proposed method with GES in this chapter shows great advantage against the comparison algorithm inspired from PSO.

Research Advance in Swarm Robotics

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.

In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

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.

"This book is a collection of the most important research achievements in swarm robotics thus far, covering the growing areas of design, control, and modeling of swarm robotics" -- Provided by publisher.

Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be used for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other’s resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. Our simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of −23% in energy consumption, −15% in latency, +18% in throughput, and +9% in fairness.

This article solves the relevant task of studying military operations on a massive wave attack by a swarm of strike drones against enemy targets in a combat zone (CZ) of a military conflict. This research solves operational and tactical tasks for planning active actions on the battlefield by applying a massive strike on enemy targets by a swarm of combat drones (kamikaze drones). Therefore, the topic of the proposed publication is relevant; it analyzes and explores the sequence of logistical military actions to plan a massive attack using waves of strike drones. The purpose of this study is to create a set of models for applied information technology that will allow planning logistical actions for the effective use of a strike drone swarm to conduct a massive wave attack on enemy targets. This article analyzes problems associated with the formation of a swarm of drones, splitting the swarm into groups, and forming waves for active combat operations on the battlefield. Enemy targets will be attacked by strike drones in the waves formation, which makes it possible to carry out further successful operational and tactical actions. This study assesses the combat capability of a swarm of drones required to defeat enemy targets in a military conflict zone. It creates a systematic representation of the sequence of actions taken to plan a massive drone swarm attack, considering the combat capability and number of drones. With the help of military experts, the composition of the actual CZs is formed, in which the targets must be hit in the first place. The swarm of drones is rationally divided into groups that are directed to a set of targets in the CZs, considering the combat capability of drones to defeat targets. Waves of strike drones are planned to maximize damage to enemy targets. This research analyzes possible directions of drone movement toward enemy targets despite military threats. The models form flight routes of drones swarming to enemy targets under the conditions of possible anti-drone actions. An agent model was created using the Any Logic platform to simulate drone flight and form the routes of the drone swarm groups. This article presents an illustrated example of planning logistical actions to use waves of strike drone swarms for a massive attack on enemy targets. The scientific novelty of this study is related to solving the relevant problem of preparing and planning logistical actions for a massive attack on enemy targets using waves of strike drones by creating a set of optimization and simulation models that contribute to the effectiveness of further military attack operations on the battlefield. The results of the study could be used by military leaders to plan the use of a drone swarm to launch a massive attack on enemy targets in a military conflict zone.

In recent years drones have become more widely used in military and non-military applications. Automation of these drones will become more important as their use increases. Individual drones acting autonomously will be able to achieve some tasks, but swarms of autonomous drones working together will be able to achieve much more complex tasks and be able to better adapt to changing environments. In this paper we describe an example scenario involving a swarm of drones from a military coalition and civil/humanitarian organisations that are working collaboratively to monitor areas at risk of flooding. We provide a definition of a swarm and how they can operate by exchanging messages. We define a flexible set of policies that are applicable to our scenario that can be easily extended to other scenarios or policy paradigms. These policies ensure that the swarms of drones behave as expected (e.g., for safety and security). Finally we discuss the challenges and limitations around policies for autonomous swarms and how new research, such as generative policies, can aid in solving these limitations.

With the trend of developing more and more applications for Unmanned Aerial Vehicles (UAV), several research projects have considered new missions where single UAVs are replaced by swarms of drones. Although today regulations do not take into account such scenarios, implementation of an efficient security policy appears mandatory before operating a swarm of drones in open spaces.Consequently, this paper introduces an architecture for providing security features through the use of software defined network (SDN) technologies. To validate our approach, we compare the routing performance of our architecture with a classical solution based on the AODV routing protocol and the use of iptables rules. The results confirm the suitability of a SDN solution in this context. Finally, we present how it may be used to improve network security for a swarm of cooperative drones.

We present a number of breakthroughs for coordinated motion planning, in which the objective is to reconfigure a swarm of labeled convex objects by a combination of parallel, continuous, collision-free translations into a given target arrangement. Problems of this type can be traced back to the classic work of Schwartz and Sharir (1983), who gave a method for deciding the existence of a coordinated motion for a set of disks between obstacles; their approach is polynomial in the complexity of the obstacles, but exponential in the number of disks. Despite a broad range of other non-trivial results for multi-object motion planning, previous work has largely focused on sequential schedules, in which one robot moves at a time, with objectives such as the number of moves; attempts to minimize the overall makespan of a coordinated parallel motion schedule (with many robots moving simultaneously) have defied all attempts at establishing the complexity in the absence of obstacles, as well as the existence of efficient approximation methods. We resolve these open problems by developing a framework that provides constant-factor approximation algorithms for minimizing the execution time of a coordinated, parallel motion plan for a swarm of robots in the absence of obstacles, provided their arrangement entails some amount of separability. In fact, our algorithm achieves constant stretch factor: If all robots want to move at most d units from their respective starting positions, then the total duration of the overall schedule (and hence the distance traveled by each robot) is O(d). Various extensions include unlabeled robots and different classes of robots. We also resolve the complexity of finding a reconfiguration plan with minimal execution time by proving that this is NP-hard, even for a grid arrangement without any stationary obstacles. On the other hand, we show that for densely packed disks that cannot be well separated, a stretch factor Omega(N^{1/4}) may be required. On the positive side, we establish a stretch factor of O(N^{1/2}) even in this case. The intricate difficulties of computing precise optimal solutions are demonstrated by the seemingly simple case of just two disks, which is shown to be excruciatingly difficult to solve to optimality.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. In this work, we developed a robot controller using Reynolds' flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we proposed using a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots, and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds' rules in a swarm of e-puck robots. Our algorithm outperformed this method in terms of cohesion performance and aggregation completion time.

This paper studies self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies, briefly describing its sensing and communication abilities. In particular, we describe a scalable method that allows the robots to sense the orientations of their neighbors using a digital compass and wireless communication. Then we propose a behavior for a swarm of robots that creates self-organized flocking by using heading alignment and proximal control. The flocking behavior is observed to operate in three phases: alignment, advance, and avoidance. We evaluate four variants of this behavior by setting its parameters to extreme values and analyze the performance of flocking using a number of metrics, such as order and entropy. Our results show that, the flocking behavior obtained under appropriate parameter values, is quite robust and generates successful self-organized flocking in constraint environments.

In this work, we propose a method for self-organized adaptive task partitioning in a swarm of robots. Task partitioning refers to the decomposition of a task into less complex subtasks, which can then be tackled separately. Task partitioning can be observed in many species of social animals, where it provides several benefits for the group. Self-organized task partitioning in artificial swarm systems is currently not widely studied, although it has clear advantages in large groups. We propose a fully decentralized adaptive method that allows a swarm of robots to autonomously decide whether to partition a task into two sequential subtasks or not. The method is tested on a simulated foraging problem. We study the method’s performance in two different environments. In one environment the performance of the system is optimal when the foraging task is partitioned, in the other case when it is not. We show that by employing the method proposed in this paper, a swarm of autonomous robots can reach optimal performance in both environments.KeywordsAdaptive MethodAverage ThroughputAutonomous RobotSwarm SizeSwarm RoboticThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.