Heterogeneous Swarm Research Articles

Modeling and Solving the Dynamic Task Allocation Problem of Heterogeneous UAV Swarm in Unknown Environment

As a NP‐hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle (UAV) swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real‐time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf pack algorithm (BWPA) to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost‐effectiveness ratio and task execution time window. In addition, one‐dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm (WPA) is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems.

Global-to-Local Design for Self-Organized Task Allocation in Swarms

Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through mathematical modeling to predict the system behavior is generally assumed to be the grand challenge of swarm robotics. We propose to approach this problem by programming directly at the swarm level. Key to this solution is the use of heterogeneous swarms that combine appropriate subsets of agents whose hard-coded agent behaviors have known global effects. Our novel global-to-local design methodology allows to compose heterogeneous swarms for the example application of self-organized task allocation. We define a large but finite number of local agent controllers and focus on the global dynamics of behaviorally heterogeneous swarms. The user inputs the desired global task allocation for the swarm as a stationary probability distribution of agents allocated over tasks. We provide a generic method that implements the desired swarm behavior by mathematically deriving appropriate compositions of heterogeneous swarms that approximate these global user requirements. We investigate our methodology over several task allocation scenarios and validate our results with multiagent simulations. The proposed global-to-local design methodology is not limited to task allocation problems and can pave the way to formal approaches to design other swarm behaviors.

Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms

Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.

Loosening Control—A Hybrid Approach to Controlling Heterogeneous Swarms

Large pervasive systems, deployed in dynamic environments, require flexible control mechanisms to meet the demands of chaotic state changes while accomplishing system goals. As centralized control approaches may falter in environments where centralized communication and knowledge may be impossible to implement, researchers have proposed decentralized control methods that leverage agent-driven, self-organizing behaviors, to achieve reliable, flexible systems. This article presents and compares the performance of three decentralized control approaches in the online multi-object k -assignment problem. In this domain, a set of sensors is tasked to detect and track an unknown and changing set of targets. Results show that a proposed hybrid approach that incorporates supervisory devices within the population while allowing semi-autonomous operations in non-supervisory devices produces a flexible and reliable system capable of both high detection and coverage rates.

Movement of a group of unmanned aerial vehicles in formation

This paper presents a method for organising the movement of unmanned aerial vehicles in formation. In the presented method of UAV collisions are prevented by a modification of the potential field method. The movement of the group is accomplished by incrementing the origin coordinates of the formation at each iteration of data exchange between UAVs. This paper presents a graph showing the negligible positioning error of drones in formation over time as the heterogeneous swarm of quadcopters moves towards the target position. Simulation results show that the flight time of a group of UAVs to the target swarm position using the proposed method is longer than using the master-slave method. The model of a Wi-Fi network as an information exchange network between UAVs was used in the developed simulation environment. This approach facilitates the implementation of the presented method on real aircrafts.

Fieldswarm technology for tillage and crop care

Abstract Today agriculture equipment is characterized by a high level of productivity and automation. Tractor-implement systems establish productivity by increasing working width and higher operational speed supported by larger storage volumes and increased tractor power availability. Dimensions and weight of tractor – implement systems have become the most important limitations for further growing productivity. A trend from Bigger, Faster, Wider towards Smart, Connected and Modular is under discussion. Modular and scalable systems with a high and variable degree of autonomy have potential to deliver configuration capability regarding actual task and field conditions while providing scalability of peak performance, productivity and economy. Possible machine concepts can consist of a tractor with automated and modular process units and / or autonomous self-propelled systems working as single unit or in a fleet (swarm). Swarm operation requires an adaptive and multimodal frontend to manage and lead a heterogeneous swarm of smart process applications. The example Feldschwarm, shown in the paper, is designed to the requirements of tillage and seeding in a first step.

Подход к организации гетерогенного роя устройств

The paper describes an approach to management of heterogeneous swarm of cyber-physical devices. This approach includes 3 functional levels: the aggregation level, the peripheral level and the data collection level. Variants of using this approach are considered: a crisis management network, a robotic complex for monitoring premises and a system for detecting security incidents in premises.

Development of Visual Servoing-Based Autonomous Docking Capabilities in a Heterogeneous Swarm of Marine Robots

This paper describes the design, development, and testing of both hardware and software for a visual servoing-based system that enables agents within a heterogeneous marine robotic swarm to share energy. The goal of this system is prolonging the active operational time of the swarm as a whole, allowing it to perform long-term environmental monitoring and data collection missions. The implementation presented in the paper features an over-actuated autonomous surface platform docking up to four floating sensor nodes at a time and replenishing their batteries using wireless inductive charging. Mechanical solutions for each robot segment related to the docking procedure are presented, along with pertinent high-level and low-level control structures. A node featuring an extended Kalman filter and additional heuristics is used to fuse measurements from a neural network trained for object detection and a hue thresholding image processing algorithm, in order to track the docking target and achieve visual servoing. Finally, experimental results in both a controlled environment and challenging conditions on-site are presented. Once deployed, the developed system successfully enables the approach and docking of the designated target, showing its feasibility in different real-life conditions.

Stability Analysis of Swarm Heterogeneous Robots with Limited Field of View

This paper presents a stability analysis of swarm robots, a group of multiple robots. In particular, we focus on robot swarms with heterogeneous abilities, in which each robot has a different sensing range and physical limitations, including maximum velocity and acceleration. In addition, each robot has a unique sensing region with a limited angle field of view. We previously proposed a decentralized navigation method for such heterogeneous swarm robots consisting of one leader and multiple followers. With the decentralized navigation method, a single leader can navigate for followers while maintaining connectivity and satisfying the physical limitations unique to each robot; i.e., each follower has a target robot and follows it without violating its physical limitations. In this paper, we focus on a stability analysis of such swarm robots. When the leader moves at a constant velocity, we mathematically prove that the shape and orientations of all robots eventually converge to the equilibrium state. For this, we must first prove that the equilibrium state exists. Then, we show the convergence of the state to its equilibrium. Finally, we carry out experiments and numerical simulations to confirm the stability analysis, i.e., the convergence of the swarm robots to the equilibrium states.

A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks.

This paper presents a novel autonomous environmental monitoring methodology based on collaboration and collective decision-making among robotic agents in a heterogeneous swarm developed within the project subCULTron, tested in a realistic marine environment. The swarm serves as an underwater mobile sensor network for exploration and monitoring of large areas. Different robotic units enable outlier and fault detection, verification of measurements and recognition of environmental anomalies, and relocation of the swarm throughout the environment. The motion capabilities of the robots and the reconfigurability of the swarm are exploited to collect data and verify suspected anomalies, or detect potential sensor faults among the swarm agents. The proposed methodology was tested in an experimental setup in the field in two marine testbeds: the Lagoon of Venice, Italy, and Biograd an Moru, Croatia. Achieved experimental results described in this paper validate and show the potential of the proposed approach.

Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

When a solar ship is navigating in the ocean, the swaying motion of a photovoltaic panel will affect the output power of the photovoltaic (PV) power generation system more frequently and violently. In addition to considering multiple climatic factors, this paper also adopts a ship swaying motion and radiation level of sunlight to establish a suitable calculation model for the output power of photovoltaic systems, which are rarely considered at the same time in previous studies, and also to make ultrashort-term power predictions. Furthermore, this paper proposes a multilayer heterogeneous particle swarm optimization (PSO) algorithm to design the weights and thresholds of a long short-term memory (LSTM) neural network to improve the accuracy of forecasting the changes of a photovoltaic panel’s angle, which is used for accurate power output prediction for the purpose of power planning. The case analysis shows the effectiveness of the algorithm, which provides a more reliable method for designing a power prediction system.

Vehicle-in-the-Loop Framework for Testing Long-Term Autonomy in a Heterogeneous Marine Robot Swarm

A heterogeneous swarm of marine robots was developed with the goal of autonomous long-term monitoring of environmental phenomena in the highly relevant ecosystem of Venice, Italy. As logistics are a continuing challenge in the field of marine robotics, especially when dealing with a large number of agents to be collected and redeployed per experimental run, an approach is needed that provides the benefits of simulation while also reflecting the complexity of the real world. This paper focuses on the development of a vehicle-in-the-loop test environment in which a surface station simulates and transmits the data of any number of simulated agents, while a real marine platform operates based on the received information. Several experimental runs of a specific use-case test scenario using the developed framework and carried out in the field are described and their results are examined.

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.

Autonomous in-orbit satellite assembly from a modular heterogeneous swarm

This paper presents a decentralized, distributed guidance and control scheme to combine a heterogeneous swarm of component satellites into a large satellite structure. The component satellites for the heterogeneous swarm are chosen to promote flexibility in final shape inspired by crystal structures and Islamic tile art. After the ideal fundamental building blocks are selected, basic nanosatellite-class satellite designs are made to assist in simulations involving attitude control. The Swarm Orbital Construction Algorithm (SOCA) is a guidance and control algorithm to allow for the limited type heterogeneity and docking ability required for in-orbit assembly. The algorithm consists of two parts, a distributed auction which uses barrier functions to ensure the proper agent selection for each target, and a trajectory generation portion which leverages model predictive control and sequential convex programming to achieve optimal collision-free trajectories to the desired target point even with nonlinear system dynamics. The optimization constraints use a boundary layer to determine whether the collision avoidance or the docking constraints should be applied. The algorithm was tested in a simulated perturbed 6-DOF spacecraft dynamic environment for planar and out-of-plane final structures and on two robotic platforms, including a swarm of frictionless spacecraft simulation robots.

Code Division Multiple Access Wireless Power Transfer for Energy Sharing in Heterogenous Robot Swarms

Energy is one of the key enablers for robotic swarms. The feasibility of heterogeneous swarms to perform complex missions depends upon the energy stored in the robot batteries. Allowing multiple access energy transfer (MAET) in robot swarms provides more flexibility to accomodate uncertain missions. In this paper, we present a MAET solution for heterogeneous swarms using code division multiple access wireless power transfer (CDMA-WPT). CDMA-WPT allows robots in heterogeneous swarms to exchange energy while in motion, allows faster charging rates by enabling multiple robots to exchange energy, is easily scalable, offers power flow selectivity to enable energy encryption, and does not require a centralized controller. The encoding scheme, power flow model, and code construction algorithm for a particular implementation of CDMA-WPT is presented. The proof of concept is verified using a hardware implementation for a heterogeneous swarm of four robots. Finally, the application of CDMA-WPT to a heterogeneous swarm with 30 robots is presented.

A Novel Heterogeneous Swarm Reinforcement Learning Method for Sequential Decision Making Problems

Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decision Makers (DMs), such RL algorithms may be designed for single-agent systems or multi-agent systems that either consist of agents with individual goals and decision making capabilities, which are influenced by other agent’s decisions, or behave as a swarm of agents that collaboratively learn a single objective. Many studies have been conducted in this area; however, when concentrating on available swarm RL algorithms, one obtains a clear view of the areas that still require attention. Most of the studies in this area focus on homogeneous swarms and so far, systems introduced as Heterogeneous Swarms (HetSs) merely include very few, i.e., two or three sub-swarms of homogeneous agents, which either, according to their capabilities, deal with a specific sub-problem of the general problem or exhibit different behaviors in order to reduce the risk of bias. This study introduces a novel approach that allows agents, which are originally designed to solve different problems and hence have higher degrees of heterogeneity, to behave as a swarm when addressing identical sub-problems. In fact, the affinity between two agents, which measures the compatibility of agents to work together towards solving a specific sub-problem, is used in designing a Heterogeneous Swarm RL (HetSRL) algorithm that allows HetSs to solve the intended SDMPs.

A Control Architecture for Robot Swarms (AMEB)

A control architecture for heterogeneous robot swarms (AMEB) is proposed in this work. The architecture manages the processes that occur in the system and its main objectives are to allow the emergence and self-organization in the group. It is structured in three levels: an individual level, a collective level under the philosophy of emergent behavior, and a level for the management of learning and knowledge. The architecture includes a behavioral component that allows the inclusion of emotions in the members of the swarm. Finally, it described a method for verifying the occurrence of the emergence in the swarm, using fuzzy cognitive maps.

Controlling Parent Systems Through Swarms Using Abstraction

This study considers the control of parent-child systems where a parent system is acted on by a set of controllable child systems (i.e. a swarm). Examples of such systems include a swarm of robots pushing an object over a surface, a swarm of aerial vehicles carrying a large load, or a set of end effectors manipulating an object. In this paper, a general approach for decoupling the swarm from the parent system through a low-dimensional abstract state space is presented. The requirements of this approach are given along with how constraints on both systems propagate through the abstract state and impact the requirements of the controllers for both systems. To demonstrate, several controllers with hard state constraints are designed to track a given desired angle trajectory of a tilting plane with a swarm of robots driving on top. Both homogeneous and heterogeneous swarms of varying sizes and properties are considered to test the robustness of this architecture. The controllers are shown to be locally asymptotically stable and are demonstrated in simulation.

Approach to organizing of a heterogeneous swarm of cyber-physical devices to detect intruders

Abstract The paper presents the approach to the organization of a heterogeneous swarm of cyberphysical devices to detect and monitor intruders. The developed system is focused on implementation in a software-hardware environment and includes three functional levels: aggregation, peripheral, and data collection. Several use cases, which are now under development, are demonstrated: a crisis management network, a robotic complex for monitoring premises, a system for detection of security incidents in premises.

Semi-Global Cooperative Output Regulation Problem for Heterogeneous Swarm Systems With Input Saturation Under Switching Network

In this paper, the semi-global cooperative output regulation problem of heterogeneous swarm systems subject to saturation under switching network is investigated. Like the common consensus problems, the swarm systems consist of some followers cannot access the state of the leader directly. But the difference is that some even cannot measure the state of the leader all the time. By utilizing the distributed output feedback control method, the low-gain feedback technique and the output regulation theories, we establish two sets of consensus control laws, respectively, based on the state feedback method and the output feedback method, to solve the output consensus problems in this kind. Besides, the global leader-following output consensus problem under switching network is proved to be a special case of semi-global cooperative output regulation problem under switching network, and as a consequence, it can also be solved by our method. A simulation example is proposed to illustrate the results very well.