Swarm Of Agents Research Articles

Reference Tracking for Multiagent Systems Using Model Predictive Control

In this note, the reference tracking problem for teams of unmanned vehicles subject to formation constraints is solved via a model predictive control (MPC) algorithm built up in a distributed fashion. By exploiting the properties deriving from a novel kinematic description of the swarm agents, the receding horizon control (RHC) approach is properly adapted to deal with tracking and formation constraints. In particular, neighbor interactions are translated into convex conditions, thanks to an in-depth analysis of the geometric properties arising from the combined use of swarm kinematics and state predictions tubes. Experimental results on Elisa-3 robots show the applicability and effectiveness of the proposed control architecture.

Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating

AbstractThis paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called exhibited decision, determines the agent’s state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems.

Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach

We develop a probabilistic control algorithm, GTLProCo, for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The resulting algorithm not only achieves decentralized control of the swarm but also significantly improves scalability over state-of-the-art existing algorithms. Specifically, we study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently-proposed language called graph temporal logic (GTL). By constraining the distribution of the swarm over the nodes of the graph, GTL can specify a wide range of properties, including safety, progress, and response. GTLProCo, with a computational complexity agnostic to the number of agents comprising the swarm, controls the density distribution of the swarm in a decentralized and probabilistic manner. To this end, it synthesizes a time-varying Markov chain modeling the time evolution of the density distribution under the GTL constraints. We first identify a subset of GTL, namely reach-avoid specifications, for which we can reduce the synthesis of such a Markov chain to either linear or semi-definite programs. Then, in the general case, we formulate the synthesis of the Markov chain as a mixed-integer nonlinear program (MINLP).We exploit the structure of the problem to provide an efficient sequential mixed-integer linear programming scheme with trust regions to solve the MINLP. We empirically demonstrate that our sequential scheme is at least three orders of magnitude faster than off-the-shelf MINLP solvers and illustrate the effectiveness of GTLProCo in several swarm scenarios.

Appropriate noise addition to metaheuristic algorithms can enhance their performance

Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation–Projection (HPP), to enhance an algorithm’s exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains (ACO). Extensive numerical experiments show that the three algorithms with the HPP strategy outperform the original versions with 60–80% the times with significant margins.

Energy-Aware Tracking and Jamming Rogue UAVs Using a Swarm of Pursuer UAV Agents

Over the recent years, there have been tremendous advances in the development of unmanned aerial vehicles (UAVs), that are now attracting increased interest for a number of commercial and military applications. However, usage of such autonomous vehicles over sensitive areas poses significant security and/or public safety threats. This article investigates a system that can counter such threats by utilizing a swarm of pursuer UAV agents that cooperate in tracking and jamming multiple rogue UAVs in an attempt to interfere with their operation. The proposed framework aims to select the best control actions for each UAV, in terms of joint mobility and jamming power, succeeding in optimally tracking and successfully jamming the rogue UAVs over time, while also ensuring that the power received by the pursuer UAVs remains below predefined interference levels. Additionally, the proposed framework minimizes the total energy consumption of the swarm of UAV agents (accounting for both jamming and propulsion energy consumption). The applicability and effectiveness of the proposed approach is validated through extensive simulation experiments for different pursuer-target scenarios, with UAVs utilizing both fixed- and rotary-wing technologies.

Formation Control Over Delaunay Triangulation Networks With Guaranteed Collision Avoidance

This article addresses the collision avoidance of networked swarms of mobile agents. A gradient-based control scheme with artificial potential fields guides all the agents into a distance-based formation. While, in the relevant literature, unbounded forces are used to achieve collision avoidance, this article shows a way to guarantee collision avoidance that respects control input limitations. To this end, it is proposed to use Morse potential functions, and it is shown how to parameterize them appropriately. The results are extended to the case that a switching proximity communication network in form of a Delaunay triangulation is applied to the networked system. It is shown that under which conditions the collision avoidance is preserved among the overall network. The proposed method is evaluated experimentally with mobile robots.

Evaluation of the iterative method of task distribution in a swarm of unmanned aerial vehicles in a clustered field of targets

The purpose of this article is to evaluate the performance of the developed iterative method for distributing tasks in a swarm of UAVs in a clustered target field. This article presents an iterative method for distributing tasks among agents of a swarm of unmanned aerial vehicles (UAVs) in a clustered target field, when the number of tasks significantly exceeds the number of agents. The main tasks solved by UAVs are surveillance and reconnaissance, detection of dangerous objects or places of emergency, search for victims, etc. The efficiency of solving the problems listed above is achieved by the simultaneous use of a group of UAVs, the elements (agents) of which can carry out the tasks of inspecting and scanning various areas in parallel. At the same time, the number of tasks can significantly exceed the number of UAV swarm agents. To organize the work of a swarm of UAVs in this case, it is necessary to solve the problem of labor division, considering the conditions of the problem. The results showed the high efficiency of the proposed task distribution method according to the criterion of minimizing the travel distance of agents.

A Decision-making Method for Swarm Agents in Attack-defense Confrontation

The cooperative decision-making of swarm agents has attracted extensive attention from researchers due to its potential applications in multidisciplinary engineering problems. This paper studies a confrontation problem called asymmetric attack-defense confrontation (i.e., considering the difference in capability and quantity between agents and targets). The objective is to develop an effective self-organized swarm confrontation decision-making method. The decision-making process consists of task allocation decision and swarm motion decision. At each decision-making step, firstly, each agent forms a coalition with other agents autonomously by using a proposed hedonic coalition formation algorithm according to the attribute of targets. Thus, the agents assigned to the same target form a coalition, and swarm agents form several disjoint coalitions. Then, based on the allocated results, the agents are steered toward the corresponding target by a combat stimulus and a proposed selected interaction swarm algorithm. Finally, while the targets are within the agents’ attack radius, the agents execute the confrontation decision. Simulation results show the effectiveness of the designed method.

On the Emergent Hypocycloidal and Epicycloidal Formations in a Swarm of Double Integrator Agents

In this letter, a multi-agent system with emergent hypocycloidal and epicycloidal behaviors is analyzed. Agents are assumed to be modeled as double integrators with an acceleration law designed so that the swarm exhibits cycloidal like trajectories. The resulting control protocol benefits from speeds and positions coupling among neighbors agents to let each agent perform hypocycloidal or epicycloidal curves according to the choice of the controller parameters and the agents initial conditions. As a first step, the model properties are obtained assuming that the communication topology of the multi-agent system is defined by a complete graph. As a further step, the model main features are extended by relaxing the complete connection assumption and replacing it with a more realistic one in which a connected graph is considered. This latter step is achieved by means of a consensus-based estimation of the swarm centroid, so that to virtualize the complete graph starting from a connected one. Simulations have been performed to show the properties of the obtained agents evolution.

Mobile Agents-Based Collaborative Perception for Pollution Source Localization in Complex Environments

In this letter, we present a multiagent collaborative perception scheme for pollution source localization in complex environments. To ensure reliable connectivity, a crowd-sensing model is designed to adaptively adjust the sensing range of the swarm of mobile agents. While by introducing the residual energy of agents, a modified mayfly algorithm (MA) is adopted to guide mobile agents to move toward the pollutant source, which can give attention to both energy efficiency and localization accuracy. Besides, artificial potential field is utilized to construct the repulsive force field of each mobile agent to avoid collisions in the process of moving toward the pollution source. Experimental simulations validate that the proposed method has better performance in localization accuracy, collision avoidance, and energy efficiency.

Swarm formation for perimeter surveillance in rectangular strips: a distributed model predictive approach

In this paper, a distributed control architecture is presented for addressing the cooperative perimeter surveillance of rectangular areas for multi-agent system whose dynamics is described by double integrator models subject to exogenous bounded disturbances. In particular, a novel methodology to generate proper state trajectories of a swarm of agents is provided and then exploited as references for the underlying model predictive controllers bank. Specifically, the swarm is first driven along the perimeter of a rectangular strip and next imposed to enter a given containment region while remaining simultaneously outside a forbidden region around a prescribed target. Steady-state conditions are analyzed and sufficient conditions derived in terms of the control law parameters. Simulation results put in light the main properties of the control architecture that is designed to adequately switch between two different distributed algorithms in order to guarantee constraints satisfaction within the capturing region despite any disturbance realization.

Formation tracking control design for uncertain artificial swarm systems under inequality constraints: Leakage and dead-zone type

The paper proposes a formation tracking control method for the uncertain artificial swarm systems under the inequality constraints. Not only can the agents perform swarm behaviors (e.g., convergence, formation and avoidance of collision), but they can also track the fixed targets in a constrained area (which is formulated as the inequality constraints, such as unilateral constraint and bilateral constraint.). The swarm behaviors are creatively considered as the servo constraints or the control objectives for the swarm agents. Based on the Udwadia–Kalaba (U–K) equation, those prescribed behaviors are realized by a model-based control design (that is the servo constraints force model-based feedforward control). To deal with the inequality constraints in the formation tracking process, a differential homeomorphism transformation is used to relieve the environmental constraints for the swarm agents. Moreover, the uncertainty of the swarm agents (i.e., the parameter uncertainty in modeling and the external disturbances) is considered, which is time-varying and unknown (but bounded). An uncertainty estimation method with dead-zone and leakage term is designed to calculate the possible upper bound of the uncertainty. In virtue of the estimated upper bound of the uncertainty, a robust control is designed for the uncertain swarm agents to obey the prescribed swarm behaviors in the formation tracking task. The system performances of the artificial swarm systems under the proposed control are theoretically guaranteed by a range of rigorous theorems and numerically verified by the simulations of three agents.

Distributed group cooperation with multi-mechanism fusion in an adversarial environment

Distributed group cooperation is a challenging problem for a swarm of agents with local information. In this paper, a multi-mechanism fusion strategy for the pursuer group is addressed to capture a faster evader cooperatively. First, the reach-avoid game is established as an adversarial environment. And the practical factors such as perception, communication and maneuverability are considered. Then, the multi-mechanism fusion strategy, which contains six sub-mechanisms that are divided into the core mechanism and auxiliary mechanism, is proposed as a mechanism library to jointly guide the pursuer behaviors. The behavior tendency function of each sub-mechanism is designed as the objective function and the particle swarm algorithm is applied for optimal decision. Meantime, an evasion strategy is also designed to examine the strategy efficiency of the pursuer group. Finally, a case study is conducted to verify the effect of the proposed method. The discussion and comparison are raised based on the speed ratio and formation spacing. The results show that the proposed method can achieve good performance with an appropriate combination of the speed ratio and formation spacing when considering the success ratio and collision ratio simultaneously.

Path Planning and Formation Control for UAV-Enabled Mobile Edge Computing Network.

Recent developments in unmanned aerial vehicles (UAVs) have led to the introduction of a wide variety of innovative applications, especially in the Mobile Edge Computing (MEC) field. UAV swarms are suggested as a promising solution to cope with the issues that may arise when connecting Internet of Things (IoT) applications to a fog platform. We are interested in a crucial aspect of designing a swarm of UAVs in this work, which is the coordination of swarm agents in complicated and unknown environments. Centralized leader–follower formations are one of the most prevalent architectural designs in the literature. In the event of a failed leader, however, the entire mission is canceled. This paper proposes a framework to enable the use of UAVs under different MEC architectures, overcomes the drawbacks of centralized architectures, and improves their overall performance. The most significant contribution of this research is the combination of distributed formation control, online leader election, and collaborative obstacle avoidance. For the initial phase, the optimal path between departure and arrival points is generated, avoiding obstacles and agent collisions. Next, a quaternion-based sliding mode controller is designed for formation control and trajectory tracking. Moreover, in the event of a failed leader, the leader election phase allows agents to select the most qualified leader for the formation. Multiple possible scenarios simulating real-time applications are used to evaluate the framework. The obtained results demonstrate the capability of UAVs to adapt to different MEC architectures under different constraints. Lastly, a comparison is made with existing structures to demonstrate the effectiveness, safety, and durability of the designed framework.

Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

PSO + FL = PAASO: particle swarm optimization + federated learning = privacy-aware agent swarm optimization

In this paper, we present an unified framework that encompasses both particle swarm optimization (PSO) and federated learning (FL). This unified framework shows that we can understand both PSO and FL in terms of a function to be optimized by a set of agents but in which agents have different privacy requirements. PSO is the most relaxed case, and FL considers slightly stronger constraints. Even stronger privacy requirements can be considered which will lead to still stronger privacy-preserving solutions. Differentially private solutions as well as local differential privacy/reidentification privacy for agents opinions are the additional privacy models to be considered. In this paper, we discuss this framework and the different privacy-related alternatives. We present experiments that show how the additional privacy requirements degrade the results of the system. To that end, we consider optimization problems compatible with both PSO and FL.

Noise-resistant and scalable collective preference learning via ranked voting in swarm robotics

Swarm robotics studies how to use large groups of cooperating robots to perform designated tasks. Given the need for scalability, individual members of the swarm usually have only limited sensory capabilities, which can be unreliable in noisy situations. One way to address this shortcoming is via collective decision-making, and utilizing peer-to-peer local interactions to enhance the behavioral performances of the whole swarm of intelligent agents. In this paper, we address a collective preference learning scenario, where agents seek to rank a series of given sites according to a preference order. We have proposed and tested a novel ranked voting-based strategy to perform the designated task. We use two variants of a belief fusion-based strategy as benchmarks. We compare the considered algorithms in terms of accuracy and precision of decisions as well as the convergence time. We have tested the considered algorithms in various noise levels, evidence rates, and swarm sizes. We have concluded that the proposed ranked voting approach is significantly cheaper and more accurate, at the cost of less precision and longer convergence time. It is especially advantageous compared to the benchmark when facing high noise or large swarm size.

A Generalized Gazi–Passino Model With Coordinate-Coupling Matrices for Swarm Formation With Rotation Behavior

In this article, a kinematic continuous time swarm model is considered by extending the results in the paper “A class of attractions/repulsion functions for stable swarm aggregations” (as shown in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">International Journal of Control</i> , vol. 77, pages 1567–1579). The extension regards a more general class of attraction/repulsion functions that can be used to explain the formation of rotational patterns in a swarm of agents. The main characteristic of this model is the introduction of two coordinate-coupling matrices weighting both the attractive and the repulsive interactions among the agents. A stability analysis is presented to characterize the swarm in terms of size and cohesiveness. Moreover, a class of attraction/repulsion functions is presented to guarantee the cohesiveness of the swarm in a polytopic region and a steady-state motion of the agents in a rotational frame around the swarm centroid. Numerical simulations are provided to illustrate the obtained results.

Doubly Stochastic Pairwise Interactions for Agreement and Alignment

Random pairwise encounters often occur in large populations, or groups of mobile agents, and various types of local interactions that happen at encounters account for emergent global phenomena. In particular, in the fields of swarm robotics, sociobiology, and social dynamics, several types of local pairwise interactions were proposed and analysed leading to spatial gathering or clustering and agreement in teams of robotic agents coordinated motion, in animal herds, or in human societies. We here propose a very simple stochastic interaction at encounters that leads to agreement or geometric alignment in swarms of simple agents, and analyse the process of converging to consensus. Consider a group of agents whose evolve in time by pairwise interactions: the state of an agent is either a real value (a randomly initialised position within an interval) or a vector that is either unconstrained (e.g. the location of the agent in the plane) or constrained to have unit length (e.g. the direction of the agent's motion). The interactions are doubly stochastic, in the sense that, at discrete time steps, pairs of agents are randomly selected and their new states are independently and uniformly set at random in (local) domains or intervals defined by the states of the interacting pair. We show that such processes lead, in finite expected time (measured by the number of interactions that occurred) to agreement in case of unconstrained states and alignment when the states are unit vectors.

A Locust-Inspired Model of Collective Marching on Rings.

We study the collective motion of autonomous mobile agents in a ringlike environment. The agents’ dynamics are inspired by known laboratory experiments on the dynamics of locust swarms. In these experiments, locusts placed at arbitrary locations and initial orientations on a ring-shaped arena are observed to eventually all march in the same direction. In this work we ask whether, and how fast, a similar phenomenon occurs in a stochastic swarm of simple locust-inspired agents. The agents are randomly initiated as marching either clockwise or counterclockwise on a discretized, wide ring-shaped region, which we subdivide into k concentric tracks of length n. Collisions cause agents to change their direction of motion. To avoid this, agents may decide to switch tracks to merge with platoons of agents marching in their direction. We prove that such agents must eventually converge to a local consensus about their direction of motion, meaning that all agents on each narrow track must eventually march in the same direction. We give asymptotic bounds for the expected time it takes for such convergence or “stabilization” to occur, which depends on the number of agents, the length of the tracks, and the number of tracks. We show that when agents also have a small probability of “erratic”, random track-jumping behavior, a global consensus on the direction of motion across all tracks will eventually be reached. Finally, we verify our theoretical findings in numerical simulations.