This study introduces a distributed cooperative guidance strategy enabling a swarm of missiles—modeled with detailed six-degree-of-freedom (6-DOF) dynamics—to execute a coordinated strike on a specified 2D polygonal zone embedded in 3D space. By integrating repulsion-attraction-based formation control with Proportional Navigation Guidance (PNG), the proposed method ensures collision-free trajectories and accurate terminal alignment. A key feature of the framework is its adaptive targeting region mechanism, which facilitates synchronized arrivals by aligning the swarm’s geometry with the structure of the polytopic area. Simulations confirm that the approach maintains safety and precision under realistic 3D operational conditions.

This paper presents a methodology to generate proper state trajectories of a swarm of agents, described as a single integrator model. The main goal of the proposed methodology is to route the agents into planar polygons like areas, so that to accomplish for cooperative perimeter surveillance or target capturing tasks. In particular, the agents are driven into a polygonal strip defined by a given containment external convex polygonal region and an internal forbidden convex polygonal region around a prescribed target. The control law is designed looking at a circular strip, then a conversion from circular to polygonal areas is applied to achieve the prescribed goal. 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 resulting kinematic state description.

Rigid-body industrial robotic manipulators and pneumatic compression systems are widely used in mechano-therapeutic applications. However, rigid and bulky components reduce the wearability of the system in mobile environments. The current trend in wearable robotics tends to build such devices using textiles/silicone materials thanks to their lightness, compliancy, and comfort properties. In this study, thermally powered soft fluidic actuators that utilize liquid/gas phase transition property of low boiling point liquids are investigated. Proposed actuators generate desired pressure level and have great potential to free mechano-therapy devices from such bulky components. The developed structure incorporates textile-based heaters, thin film actuator shells, and low boiling point liquids. Here, two low boiling liquids with different boiling temperature are mixed successfully in order to obtain desired boiling point. Results a revealed that actuators constructed with PE/PA membrane did not show any deterioration even after 500 cycle actuation. In order to control the system, instantaneous temperature of the textile-based heater on the actuator device is used as feedback for the on-off controller.

In this article we develop an energy based model for the dynamics of a swarm of quadrotors using Lagrangian approach. Both the individual quadrotor dynamics and the interaction dynamics between the agents in the swarm are modeled within the framework. Artificial potential energy and the corresponding potential forces are utilized for modeling the interaction between the quadrotors. Various simulations are performed to illustrate the viability and effectiveness of the developed swarm model. The kinetic and potential energy of the swarm is also illustrated.

This paper proposes a novel algorithm to deploy mobile sensors for maximization of sensing area coverage. The algorithm presented is distributed and robust to errors. Our method is dynamic and based on potential fields. Although some methods using potential fields have been proposed previously, the approach we take here is different in constructing the potential field. In particular, we utilize an entropy-based potential function and use the Newton's iteration to determine the sensor positions. Simulations show that our method is more robust to errors, and less likely to be trapped in local minima compared to some previously proposed methods.

In this article we approach the problem of distributed agreement in multi-agent systems using asynchronous particle swarm optimization (PSO) with dynamic neighborhood. The agents are considered as PSO particles which are assumed to have time-dependent neighborhoods, operate asynchronously and incur time delays during information exchange. The performance of the PSO based agreement algorithm is verified using representative numerical simulations.

This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor source invisible to the robots so that the declared source and the odor plume it generates do not interfere with the effects of other existing plumes, allowing the localization of the remaining sources. Exploration and plume tracking by the robots is achieved using a decentralized asynchronous particle swarm optimization algorithm. The divergence operator is used to declare the odor sources. A set of simulations and real world experiments are performed on two different scenarios on a controlled environment using a swarm of 5 robots to validate the proposed methodology. Results show that the virtual plume cancelation algorithm can be successfully used to find multiple odor sources, even when two plumes overlap. It can also extend the operation of many odor source localization algorithms developed for single source localization.

In this paper, potential flow calculations for incompressible inviscid flows are used to develop a collision free navigation algorithm for a robot swarm. Robots are equiped with laser scanners which are used to detect the obstacles in the environment. Then, a real-time panel method is used to calculate the streamlines of the potential flow around the complex shaped rigid objects, providing the robots with safe trajectories to the target. The swarm of robots is also forced to keep a desired formation during navigation using potential functions. Potential flow algorithms provide navigation with smooth paths to the target. The algorithm can be used in dynamic environments in real-time as the changes in the medium are detected.

In this article the main objective is to perform a search in an unknown area with multiple robots in order to determine the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing. Robots perform the search autonomously by using their own data and the information (position information and sensor readings) obtained from the other robots. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment. To achieve this task as a high-level path planning algorithm we use a decentralized and asynchronous version of the Particle Swarm Optimization (PSO) algorithm which also allows for time-varying neighborhood.

In this article we present implementation of Bacterial Foraging Optimization algorithm inspired search by multiple robots in an unknown area in order to find the region with highest chemical gas concentration as well as to build the chemical gas concentration map. The searching and map building tasks are accomplished by using mobile robots equipped with smart transducers for gas sensing called “KheNose”. Robots perform the search autonomously via bacterial chemotactic behavior. Moreover, simultaneously the robots send their sensor readings of the chemical concentration and their position data to a remote computer (a base station), where the data is combined, interpolated, and filtered to form an real-time map of the chemical gas concentration in the environment.

Mobile robots are nowadays capable of navigating in a semi-structured environment, either alone or in formations. Dealing with mobile obstacles, e.g. persons, in everyday environments poses additional challenges which are being addressed by the research community. This paper presents an experiment with a set of robots -acting as a swarm- and one person. The aim of such robot-human squad is to explore an unmapped area indoors. The swarm is programmed such that obstacle avoidance, robot avoidance and human avoidance is combined with staying in the vicinity of the human and gradually proceeding towards the end point. The robots are provided with enough sensors to perform avoidance, and determine/estimate the distance to the human. Communication is one-way only, from robots to human. The aim of the experiment is to provide insights about the real problems of the multirobot system when dealing with a non-expert person in a cooperative exploration task.

A practical solution based on multi-agent protocols for the development of real-world multi-robot applications is presented. FIPA standard protocols implemented by the JADE library provide the standard functionality for a number of tasks. Robot behaviors are built upon the player middleware. Such components provide off-the-shelf tools which allow a straightforward implementation of indoor localization and navigation tasks for a team of mobile robots. Such integration combines proven mobile robot algorithms with a distributed infrastructure, and extends the capabilities from a robot alone to a whole team of robots, thus allowing the development of cooperative applications. As a proof of concept, an auction-like goal assignment task is presented: the robot team is given a goal, and each robot proposes an estimated cost for achieving it, then the best proposal is selected. Most of the control flow is automated by the standard interaction protocols. Experimental evaluation demonstrates the advantages of combining both frameworks, for a practical yet sound development of multi-robot applications.

In this article we consider a particle swarm optimization (PSO) algorithm in which the neighbors of the particles or basically the neighborhood topology dynamically changes with time. We consider probabilistic and distance based approaches for determining the neighbors of the particles and represent the dynamic neighborhood topology by a time varying graph. Simulations of several benchmark functions are performed in order to verify the effectiveness of the algorithm.

In this article we discuss a decentralized totally asynchronous realization of the particle swarm optimization (PSO) algorithm, which is suitable for parallel implementation. The proposed method has important differences from the PSO implementations considered in the literature. In the proposed method the particles are allowed to exchange information and to update their estimates at totally independent time instants. Moreover, time delays during information exchange between particles (leading to use of outdated information) are also allowed. Furthermore, particle neighborhoods are allowed to dynamically change with time. We also provide a mathematical model of the proposed method based on results in the parallel and distributed computation literature. The performance of the proposed algorithm is tested using numerical simulations with benchmark functions.

In this correspondence, we consider a simple model of N interacting agents with fixed or time-dependent communication links. We allow for asynchronous operation and time delays in the information flow. We show that the convergence of the states of the agents to a common value will be achieved, provided that old information is uniformly purged from the system. The considered model finds an application not only in swarming but also in other fields, including synchronization and distributed decision making or consensus seeking.

In this paper, we consider a control strategy of multiagent systems, or simply, swarms, based on artificial potential functions and the sliding-mode control technique. First, we briefly discuss a "kinematic" swarm model in n-dimensional space introduced in an earlier paper. In that model, the interindividual interactions are based on artificial potential functions, and the motion of the individuals is along the negative gradient of the combined potential. After that, we consider a general model for vehicle dynamics of each agent (swarm member), and use sliding-mode control theory to force their motion to obey the dynamics of the kinematic model. In this context, the results for the initial model serve as a "proof of concept" for multiagent coordination and control (swarm aggregation), whereas the present results serve as a possible implementation method for engineering swarms with given vehicle dynamics. The presented control scheme is robust with respect to disturbances and system uncertainties.

In this correspondence, we consider a discrete time one-dimensional asynchronous swarm. First, we describe the mathematical model for motions of the swarm members. Then, we analyze the stability properties of that model. The stability concept that we consider, which matches exactly with stability of equilibria in control theory, characterizes stability of a particular position (relative arrangement) of the swarm members. We call that position the comfortable position (with comfortable intermember distances). Our swarm model and stability analysis are different from other asynchronous swarm models considered in the literature. In particular, in our analysis we employ results on contractive mappings from the parallel and distributed computation literature. The application of these results to the swarm coordination problem is important by itself since they might prove useful also in n-dimensional swarms.

In this article we specify an M-member "individual-based" continuous time swarm model with individuals that move in an n-dimensional space according to an attractant/repellent or a nutrient profile. The motion of each individual is determined by three factors: i) attraction to the other individuals on long distances; ii) repulsion from the other individuals on short distances; and iii) attraction to the more favorable regions (or repulsion from the unfavorable regions) of the attractant/repellent profile. The emergent behavior of the swarm motion is the result of a balance between inter-individual interactions and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm for different profiles and provide conditions for collective convergence to more favorable regions of the profile.