Swarm Control Algorithm Research Articles

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions

Abstract This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Swarm of Drones in a Simulation Environment—Efficiency and Adaptation

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.

Swarm Division-Based Aircraft Velocity Obstacle Optimization Considering Low-Carbon Emissions

In the pursuit of sustainable aviation, this paper presents an innovative approach that adopts a swarm division strategy to enhance and refine the velocity obstacle (VO) method, guided by a low-carbon principle. A dynamic elliptical protection zone model forms the core of this innovative approach. Specifically, this dynamic elliptical protection zone is created based on the difference in aircraft velocity, and a swarm division strategy is introduced in this process. Initially, aircraft that share the same route and type, and have similar velocities and distances, are grouped into swarms. Then, the characteristics of the swarms, such as mass points, velocities, and protection zones, are recorded. Second, the collision cone (CC) between swarms is established, and planar geometrical analysis is used to determine the optimal relief velocity and heading of aircraft on the low-carbon objective while ensuring a safe interval between aircraft in the swarm during the relief period. Additionally, a swarm control algorithm is utilized to adjust the velocity of the aircraft by a small margin. Finally, simulation experiments are conducted using Python, revealing that the swarm relief efficiency of the enhanced VO method sees a notable increase of over 33%. Concurrently, the need for adjustments decreases by an average of 32.78%, while fuel savings reach as high as 70.18%. The strategy is real-time and operational, significantly reduces the air traffic controller (ATC) workload, improves flight efficiency and safety, and contributes positively to the reduction in carbon emissions, which is beneficial for the environment.

Neural Swarm Control Algorithm for Underwater Vehicles

The paper presents the application of an evolutionary recurrent neural network to control the swarm of underwater vehicles. In the swarm, one vehicle is the leader and the others are followers. The leader leads the swarm along a predefined trajectory without regard for the followers while the followers follow the leader and avoid collisions with all other vehicles. Avoiding collisions by the swarm with external obstacles is done by changing the depth. The leader is responsible for detecting the obstacles and informing all the followers about the need to change the depth. To follow the leader, the followers use the information about the distance to it. Directional information is unavailable to them. To avoid collisions inside the swarm, the followers use shortrange sensors.

Unmanned Surface Vehicle Using a Leader–Follower Swarm Control Algorithm

To overcome the limitations of a single unmanned surface vehicle (USV), this study investigated the swarm control algorithms of USVs. Among various swarm control methods, a leader–follower swarm control method was selected and studied. The performance of the swarm algorithm proposed in this study was verified through an actual sea area test. A USV was designed and manufactured by dividing the power and communication parts. The power system was equipped with a coulometer to monitor the battery state in real time to protect the system through a switch linked to the coulometer in the case of abnormalities in the battery and to prevent accidents. In addition, a communication system was established to process the sensor data and camera image data of the USV in real time. Consequently, the desired swarm formation was achieved through the separately constructed swarm control algorithm. Before the actual sea area test, individual performance tests of each sensor were conducted. Finally, the performance of the swarm algorithm was verified by conducting a sea area test. The performance of the controllers was confirmed to be good, and the swarm formation was confirmed to be successful.

Моделирование и исследование автоматизированной складской системы с использованием роевой робототехники

The article considers application of the role-based management strategy on the example of solving the problem of warehouse automation using mobile robots. The modernization option of existing electric forklifts is proposed by adding electronic and information components to the design, swarm control algorithms are developed. Simulation of the multi-agent system was performed in the Webots environment. The study showed that with the increase in the number of agents, the execution time of the technological task decreases until it reaches a certain limit.

Distributed Swarm Control Algorithm of Multiple Unmanned Surface Vehicles Based on Grouping Method

This paper addresses the distributed swarm control problem of multiple unmanned surface vehicles (USVs) in Euclidean space with virtual leader. Firstly, to investigate the topology of the neighborhood relations between vehicles, a new time-variant topology structure is proposed. Secondly, to research the dynamic properties of the group for the case where the number of virtual leader is different, a grouping method based on cosine similarity is proposed. Thirdly, to ensure the high effeciency of information transmission and the reduction of costs, a distributed swarm control algorithm is proposed, which is mainly composed of three parts: gradient descent term, velocity consensus term and navigational feedback term. To analyze the stability of system, the concept of translation framework is introduced. Based on the properties of the Hamiltonian and LaSalle invariance principle, the stability of multiple USVs swarm motion is proved. Finally, simulation results illustrate the effectiveness of the proposed methods.

Optimal Tuning of Linear Quadratic Regulator Controller Using Ant Colony Optimization Algorithm for Position Control of a Permanent Magnet DC Motor

This paper presents the design of an optimal Linear Quadratic Regulator (LQR) controller using Ant Colony Optimization (ACO) and particle swarm optimization (PSO) methods for position control of a permanent magnet DC (PMDC) motor. In this work, Ant Colony control and particle swarm control algorithms have been utilized to set the optimal elements of the weighting matrices subjected to a proposed cost function. The proposed cost function is a combination of the quadratic performance index and integral square error. The proposed design can overcome the difficulty in setting the weighting matrices with the suitable elements. The simulation results using (Matlab Package) show that the optimal LQR controller using ACO algorithm can give excellent performance in terms of obtaining smooth and unsaturated state voltage control action that will stabilize the DC motor system performance and minimize the position tracking error of the system output. In addition, the rising time and settling time is decreased in comparison with the LQR based PSO controller performance.

Self-Adaptation of a Heterogeneous Swarm of Mobile Robots to a Covered Area

An original swarm-based method for coordination of groups of mobile robots with a focus on the self-organization and self-adaptation of the groups is presented in this paper. The method is a nature-inspired decentralized algorithm that uses artificial pheromone marks and enables the cooperation of different types of independent reactive agents that operate in the air, on the ground, or in the water. The advantages of our solution include scalability, adaptability, and robustness. The algorithm worked with variable numbers of agents in the groups. It was resistant against failures of the individual robots. A transportation control algorithm that ensured the spreading of different types of agents across exploration space with different types of environments was introduced and tested. We established that our swarm control algorithm was able to successfully control three basic behaviors: space exploration, population management, and transportation. The behaviors were able to run simultaneously, and space exploration (the main goal) was never stopped or interrupted. All these features combined in a single algorithmic package represent a framework for future development of swarm-based agent systems applicable in a broad scope of environments. The results confirmed that the algorithm can be applied to monitoring, surveillance, patrolling, or search and rescue tasks.

A distributed flocking control strategy for UAV groups

To ensure the information security of UAV groups, maximize the detection range of the UAV group and minimize the communication range, this paper improves Olfati–Saber’s flocking algorithm using virtual leader and integrates the multiple UAVs with real environment. The improved swarm control algorithm can help to make all agents accurately track the speed of virtual leaders. Then, we introduce the conception of virtual communication circle to control the communication power of each UAV, and the moving function of the target position is improved to meet the limited conditions of given communication distance, to ensure the non-collision and stable communication of the UAV during the moving process. All UAVs in the group system will keep converging, avoiding collision and the speed is the same as that of the virtual pilot. Therefore, multiple UAVs can track the virtual pilot for cluster flight and a distributed cooperative control strategy can be acquired. The simulation results show that the strategy proposed in this paper makes a better communication performance among UAVs in real time, and the UAV groups achieve the information more timely, which is proved to be an effective way to ensure the security communication among multiple UAVs.

A distributed swarm control for an agricultural multiple unmanned aerial vehicle system

In this study, we propose a distributed swarm control algorithm for an agricultural multiple unmanned aerial vehicle system that enables a single operator to remotely control a multi-unmanned aerial vehicle system. The system has two control layers that consist of a teleoperation layer through which the operator inputs teleoperation commands via a haptic device and an unmanned aerial vehicle control layer through which the motion of unmanned aerial vehicles is controlled by a distributed swarm control algorithm. In the teleoperation layer, the operator controls the desired velocity of the unmanned aerial vehicle by manipulating the haptic device and simultaneously receives the haptic feedback. In the unmanned aerial vehicle control layer, the distributed swarm control consists of the following three control inputs: (1) velocity control of the unmanned aerial vehicle by a teleoperation command, (2) formation control to obtain the desired formation, and (3) collision avoidance control to avoid obstacles. The three controls are input to each unmanned aerial vehicle for the distributed system. The proposed algorithm is implemented in the dynamic simulator using robot operating system and Gazebo, and experimental results using four quadrotor-type unmanned aerial vehicles are presented to evaluate and verify the algorithm.

Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

Advantage of cooperative traffic light control algorithms

Based on the presentation at ITS Europe Congress Glasgow 2016. Contemporary traffic light control (TLC) systems rely on sensors for detection of traffic which are costly in purchase, installation and maintenance. Emerging cooperative technology offers an attractive alternative where only one road side unit per intersection is required, instead of several infrastructure sensors per lane. However, studies showed that traffic control with cooperative detection requires a penetration rate of at least 20% to function effectively. To show the potential of cooperative traffic control, this study presents three algorithms: (i) the SWARM control algorithm, which is designed to work with very low penetration rates; (ii) an extension to the adaptive control algorithm, ImFlow, which uses cooperative data for enhanced queue modelling; and (iii) an ImFlow extension to stabilise green planning to enable green light optimal speed advice. The results from micro-simulation show a 7.8% improvement for stops and delay time over traditional adaptive control for SWARM, and 14.9% for Cooperative ImFlow. Adding planning stabilisation reduced the average perceived change for end users from 9.0-2.3%, without performance loss for the overall traffic flow. This shows the large potential of cooperative traffic control.

BEECLUST used for exploration tasks in Autonomous Underwater Vehicles

Underwater exploration is still a difficult task these days. In this paper we suggest a swarm of Autonomous Underwater Vehicles (AUVs) for such tasks. These AUVs are controlled by BEECLUST which is a swarm control algorithm inspired by honeybee behaviour. A swarm controlled by BEECLUST shows very reliable aggregation behaviour in a global optimum. In a simulation experiment we show that BEECLUST is very well adaptable for AUV swarms. Furthermore we show that BEECLUST is not only capable of showing aggregation behaviour but also is a valid tool for underwater exploration tasks.

Nanorobotic Agents Communication Using Bee-Inspired Swarm Intelligence

The main goal of this paper is to design nanorobotic agent communication mechanisms which would yield coordinated swarm behavior. Precisely we propose a bee-inspired swarm control algorithm that allows nanorobotic agents communication in order to converge at a specific target. In this paper, we present experiment to test convergence speed and quality in a simulated multi-agent deployment in an environment with a single target. This is done to measure whether the use of our algorithm or random guess improves efficiency in terms of convergence and quality. The results attained from the experiments indicated that the use of our algorithm enhance the coordinated movement of agents towards the target compared to random guess.

Towards temporal verification of swarm robotic systems

A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots; the fault tolerance inherent in having a large population of identical robots; and the self-organised behaviour of the swarm as a whole. Although robot swarms present an attractive solution to demanding real-world applications, designing individual control algorithms that can guarantee the required global behaviour is a difficult problem. In this paper we assess and apply the use of formal verification techniques for analysing the emergent behaviours of robotic swarms. These techniques, based on the automated analysis of systems using temporal logics, allow us to analyse whether all possible behaviours within the robot swarm conform to some required specification. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether temporal properties are satisfied on all the possible behaviours of the system. We target a particular swarm control algorithm that has been tested in real robotic swarms, and show how automated temporal analysis can help to refine and analyse such an algorithm.

UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention, and common swarm control methods require expensive computational power. This paper begins with a discussion on how to improve upon current user interfaces and then reviews a swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. Next, a more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the graphics processing unit (GPU) in today's graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU's pipeline [1]. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this paper presents a vision of how to combine these new interface ideas and performance enhancements into an effective swarm control interface and training simulator.