Swarm Of Autonomous Robots Research Articles

An interactive simulation of control and coordination strategies for swarms of autonomous construction robots

An interactive simulation of control and coordination strategies for swarms of autonomous construction robots

Investigating the impact of initial parameters on autonomous robot swarm movement strategies for enhanced exploration efficiency: a comprehensive study

Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution in the field, employing simulation-based analysis. The research consists of two parts: initially, robots undergo free-fall as passive entities, followed by a phase where they employ predefined movement strategies from their fall positions. The study aims to investigate how the initial position and related parameters affect movement characteristics and the ultimate swarm distribution. To achieve this objective, four parameters-radius, height, mass, and the Coefficient of Restitution-were identified, each assigned three different values. The study observes the effects of these parameters on robot motion, considering motion strategies such as Random Walk, Levy Walk, Markov Process, and Brownian Motion. Results indicate that increasing parameter values induce changes in the position values of the free-falling swarm in the first part, which is the initial position for the second part, influencing movement strategies in diverse ways. The outcomes are analyzed concerning the radial and angular spread of the robots. Radial spread measures how far swarm elements spread from their initial positions, while angular spread indicates how homogeneously the robots are distributed according to the polar angle. The study comprehensively investigates how the movement strategies of autonomous robot swarms are impacted by parameters and how these effects manifest in the results. The findings are anticipated to enhance the effective utilization of autonomous robot swarms in exploration missions.

Speed-accuracy trade-offs in best-of-n collective decision making through heterogeneous mean-field modeling.

To succeed in their objectives, groups of individuals must be able to make quick and accurate collective decisions on the best option among a set of alternatives with different qualities. Group-living animals aim to do that all the time. Plants and fungi are thought to do so too. Swarms of autonomous robots can also be programed to make best-of-n decisions for solving tasks collaboratively. Ultimately, humans critically need it and so many times they should be better at it! Thanks to their mathematical tractability, simple models like the voter model and the local majority rule model have proven useful to describe the dynamics of such collective decision-making processes. To reach a consensus, individuals change their opinion by interacting with neighbors in their social network. At least among animals and robots, options with a better quality are exchanged more often and therefore spread faster than lower-quality options, leading to the collective selection of the best option. With our work, we study the impact of individuals making errors in pooling others' opinions caused, for example, by the need to reduce the cognitive load. Our analysis is grounded on the introduction of a model that generalizes the two existing models (local majority rule and voter model), showing a speed-accuracy trade-off regulated by the cognitive effort of individuals. We also investigate the impact of the interaction network topology on the collective dynamics. To do so, we extend our model and, by using the heterogeneous mean-field approach, we show the presence of another speed-accuracy trade-off regulated by network connectivity. An interesting result is that reduced network connectivity corresponds to an increase in collective decision accuracy.

Intelligent Coordination for a Swarm of Autonomous Mobile Robots

This study addresses the challenge of formation control and navigation in swarms of mobile robots, presenting an innovative algorithm that integrates behavioral approaches with fuzzy logic to calculate behavior weights for Efficient, adaptive coordination dynamically. Using a leader-follower mechanism enhanced by a virtual leader, the algorithm enables coordinated swarm movement in complex environments. The methodology combines fuzzy logic with a behavioral strategy, allowing mobile robots to adjust their actions based on environmental cues and swarm dynamics without predefned paths. Simulated experiments show the algorithm's capability to maintain formation integrity and navigate obstacles, significantly improving swarm adaptability and operational efficiency. The findings suggest a promising direction for developing intelligent mobile robotic systems capable of complex, autonomous tasks, contributing to the field of swarm robotics with a novel approach to formation control and navigation that balances individual autonomy with collective behavior.

Evolutionary swarm formation: From simulations to real world robots

Swarms of autonomous robots have become an interesting alternative for space and aerospace applications due to their versatility, robustness, and self-organising capability. Some of those applications, such as asteroid observation, convoy escort, and counter-drone systems, rely on stable formations achieved around a central point of interest. However, the use of different numbers of robots and the existence of a wide range of initial conditions contribute to make it a challenging problem. We propose in this research work a novel approach for self-organising a swarm of autonomous robots where the members’ movements depend only on their relative position (range and bearing) obtained from their respective radio beacons. An optimisation approach based on an evolutionary algorithm is proposed to calculate the optimal swarm’s parameters, e.g. speed and attracting/repelling forces, to achieve robust formations under different initial conditions and failure rates. Experiments are conducted using realistic simulations of six case studies featuring three, five, ten, fifteen, twenty, and thirty robots. The best valued configurations were tested on 420 scenarios showing that our proposal is robust since it has always achieved the desired circular formation. Finally, we have used real E-Puck2 robots to validate the swarm’s capability of self-organising around a central point of interest as well as its resilience to robot failure, obtaining successful circular formations in all the experiments.

Perpetual torus exploration by myopic luminous robots

We study perpetual torus exploration for swarms of autonomous, anonymous, uniform, and luminous robots. We consider robots with only few capabilities. They have a finite limited vision (myopic), they can only see robots at distance one or two. We show that the problem is impossible with only two luminous robots and also with three oblivious robots (without light). We then address the problem assuming luminous robots (resp. oblivious) with visibility range one (resp. two). We design optimal solutions with respect to both the number of robots and colors when robots share a common chirality and have, respectively, vision one and two. We also present an optimal solution with respect to the number of robots when they are endowed with vision one and share no common chirality. Finally, we propose a solution for the case in which robots are oblivious, have vision two, and no common chirality that uses one additional robot.

Mutual visibility by fat robots with slim omnidirectional camera

In the existing literature of the Mutual Visibility problem for autonomous robot swarms, the adopted visibility models have some idealistic assumptions that are not consistent with practical sensing device implementations. This paper investigates the problem in the more realistic visibility model called opaque fat robots with slim omnidirectional camera. The robots are modeled as unit disks, each having an omnidirectional camera represented as a disk of smaller size. The region obstructed by a single robot in this model is a truncated infinite cone. This makes the visibility model significantly challenging compared to the previous ones. We assume that the robots have compasses that allow agreement in the direction and orientation of both axes of their local coordinate systems. The robots are equipped with visible lights which serve as a medium of communication and also as a form of memory. We present a distributed algorithm for the Mutual Visibility problem which is provably correct in the semi-synchronous setting. Our algorithm also provides a solution for Leader Election which we use as a subroutine in our main algorithm. Although Leader Election is trivial with two axis agreement in the full visibility model, it is challenging in our case and is of independent interest.

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.

Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

Merging BSP based swarm dynamics and distributed ledger technologies for smart marine infrastructures

Distributed ledger technologies, together with AI, smart systems and robotics could provide a scalable and robust platform for the smart underwater and surface marine infrastructures of the close future. They will be harbours or ports, marine farms or remote tourism facilities - only accessible through robotic avatars - for protected areas or for the elders. Autonomous Marine Vehicles, IoT networks, and humans will coexist in highly heterogeneous multivendor multiplatform environments where market transactions and complex administrative procedures will be ubiquitous. Some blockchains such as the Ethereum network are able to provide distributed scalable computing and trustable functionalities, capable of managing both technical interactions and market transactions among very diversified autonomous agents. For these reasons they seem to provide a valuable backbone for the smart marine infrastructure of the coming decades. In this paper we outline our research and innovation strategy and present our results showing the potential benefits of a subsidiary architecture integrating distributed ledger technologies with swarms of autonomous surface robots implemented by a Belief Space Planning approach.

Concept of Operations as a Boundary Object for Knowledge Sharing in the Design of Robotic Swarms

Designing a swarm of autonomous robots for commercial, military, or other purposes is a challenging engineering and human factors design effort. The challenges argue in favor of practices and tools for better integration of different engineering disciplines and for the advancement of communication between stakeholders with different interests. The Concept of Operations (ConOps) approach is widely used in Systems Engineering for this purpose. A ConOps is a high-level description of how the elements of a system and entities in its environment interact in order to achieve their stated goals. This paper will present the development of a ConOps for a swarm of autonomous robotic vehicles in the military domain to demonstrate how autonomic robotic swarms can be deployed in different military branches in the future. The proposed ConOps can be considered as a boundary object in the design, validation, or procurement of an autonomous robotic swarm system. We also propose that the ConOps should be maintained throughout the system life-cycle as an overview description and definition of overall goals and policies.

Trajectory Tracking Design for a Swarm of Autonomous Mobile Robots: A Nonlinear Adaptive Optimal Approach

This research presents a nonlinear adaptive optimal control approach to the trajectory tracking problem of a swarm of autonomous mobile robots. Mathematically, finding an analytical adaptive control solution that meets the H2 performance index for the trajectory tracking problem when controlling a swarm of autonomous mobile robots is an almost impossible task, due to the great complexity and high dimensions of the dynamics. For deriving an analytical adaptive control law for this tracking problem, a particular formulation for the trajectory tracking error dynamics between a swarm of autonomous mobile robots and the desired trajectory is made via a filter link. Based on this prior analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from a high-dimensional nonlinear partial differential equation, which is equivalent to solving the trajectory tracking problem of a swarm of autonomous mobile robots with respect to an H2 performance index. This delivered adaptive nonlinear control solution offers the advantages of a simple control structure and good energy-saving performance. From the trajectory tracking verification, this proposed control approach possesses satisfactory trajectory tracking performance for a swarm of autonomous mobile robots, even under the effects of huge modeling uncertainties.

Grid exploration by a swarm of autonomous robots with minimum repetitions

We address the terminating exploration problem where a team of k autonomous robots explores a grid such that each node of the grid is visited at least once and all robots stop moving when the task is completed. Robots considered for the task are anonymous, asynchronous, oblivious and non-communicating that have unlimited visibility and a pebble each. Visiting a node more than once by the robots during exploration is a waste of energy due to energy constraints. In this work we aim at minimizing the total number of wasteful repeated visits of the nodes that occur during exploration. Our proposed algorithm explores a grid with minimum repeated visits of nodes.

Three-Dimensional Formation Control for Robot Swarms

This article addresses a distributed 3D algorithm for coordinating a swarm of autonomous robots (ARs) to spatially self-aggregate into an arbitrary shape based on only local interactions. Each AR has local proximity sensors for measuring the relative coordinates of its nearby AR. We assume that only a single AR, called the leader, can localize itself in global coordinate systems, while accessing the arbitrary user-specified 3D shape. We further assume that the leader has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape. Our aim is to make the ARs maneuver while maintaining a user-specified 3D formation such that the network connection of all ARs is maintained during the maneuver. Our approach results in a 3D formation shape and does not need the global localization of an AR, except for the leader. To the best of our knowledge, our study is novel in the construction of a 3D formation for covering an arbitrary shape such that network connection is maintained while ARs maneuver based on local communication. We further show that the proposed control yields reliable accuracy against significant AR failures and movement error. Utilizing MATLAB simulations, we demonstrate the outperformance of the proposed formation controls.

K-Circle formation by disoriented asynchronous robots

For some k>0, the k-circle formation problem asks a swarm of autonomous mobile robots to form disjoint circles. Each such circle is restricted to be centered at one of the pre-fixed points given in the plane. Each circle must contain exactly k robots at distinct positions. The k-circle formation problem has already been studied under one axis agreement. In this work, the robots are assumed to be completely disoriented, i.e., they neither have any agreement on a global coordinate system nor have any agreement on a common chirality. The robots are identical, anonymous, oblivious, and homogeneous. They operate in Look-Compute-Move cycles under a fair asynchronous scheduler. In this setting, all the initial configurations and values of k for which the k-circle formation problem is deterministically unsolvable have been characterized. A deterministic distributed algorithm has been proposed that deterministically solves the k-circle formation problem for the remaining configurations and values of k.

Designing a Virtual Reality Testbed for Direct Human-Swarm Interaction in Aquatic Species Monitoring

Swarm robotics is a natural candidate for monitoring large and complex environments. With the aid of a human who is directly involved in monitoring and search using a teleoperated robot, swarms of autonomous robots can solve problems related to navigation and changing objectives. In this context, understanding the role of nonverbal interactions have the potential to enable rapid and bidirectional communication between humans and robots. Quantifying such interactions however, requires a repeatable and engaging experimental setup that can track human action and cognition. This paper describes a desktop virtual reality testbed designed specifically to quantify human actions, perception, and cognitive load in real time during a monitoring mission. Motivated by recent deployment of underwater robots to monitor invasive species, the testbed is designed to mimic an underwater environment within the Great Lakes with five species of fish whose appearance, locomotion, and behavior are modeled based on videos from the field. Brain activity and pupillometry data are recorded synchronously in real time to aid in the measurement of cognitive load of the human operator. To quantify human perception, empirical data of visual acuity from the literature is used to model virtual object recognition. The capabilities of the testbed are evaluated in terms of frame rate achieved as a function of number of fish and robots in the environment, and demonstrated through two examples highlighting possible uses in human-swarm interaction studies.

A Reliable Behavioral Model

This study concerns a swarm of autonomous reactive mobile robots, qualified of naïve because of their simple constitution, having the mission of gathering objects randomly distributed while respecting two contradictory objectives: maximizing quality of the emergent heap-formation and minimizing energy consumed by aforesaid robots. This problem poses two challenges: it is a multi-objective optimization problem and it is a hard problem. To solve it, one of renowned multi-objective evolutionary algorithms is used. Obtained solution, via a simulation process, unveils a close relationship between behavioral-rules and consumed energy; it represents the sought behavioral model, optimizing the grouping quality and energy consumption. Its reliability is shown by evaluating its robustness, scalability, and flexibility. Also, it is compared with a single-objective behavioral model. Results' analysis proves its high robustness, its superiority in terms of scalability and flexibility, and its longevity measured based on the activity time of the robotic system that it integrates.

Almost global convergence to practical synchronization in the generalized Kuramoto model on networks over the n-sphere

From the flashing of fireflies to autonomous robot swarms, synchronization phenomena are ubiquitous in nature and technology. They are commonly described by the Kuramoto model that, in this paper, we generalise to networks over n-dimensional spheres. We show that, for almost all initial conditions, the sphere model converges to a set with small diameter if the model parameters satisfy a given bound. Moreover, for even n, a special case of the generalized model can achieve phase synchronization with nonidentical frequency parameters. These results contrast with the standard n = 1 Kuramoto model, which is multistable (i.e., has multiple equilibria), and converges to phase synchronization only if the frequency parameters are identical. Hence, this paper shows that the generalized network Kuramoto models for n ≥ 2 displays more coherent and predictable behavior than the standard n = 1 model, a desirable property both in flocks of animals and for robot control.

Assembling of Robots in Presence of Line Obstacles with Direction-only Axes Agreement

This paper addresses the problem of assembling semi-synchronous oblivious robots in presence of horizontal line obstacles under direction only axes agreement model. In this article, a distributed algorithm is proposed for a swarm of autonomous mobile robots that are required to assemble over the boundaries of a rectangular bounded region within a finite amount of time. The robots are initially deployed randomly within the region and assumed to support the non-rigid motion. All the line-shaped obstacles are randomly scattered within the region and are assumed to be positioned horizontally, parallel to the top and bottom boundaries of the rectangular region. Though the robots are assumed to have unlimited visibility, these opaque obstacles restrict their vision. The existing solution to this problem assumes full axes agreement among the robots, whereas, the proposed solution assumes direction only axis agreement, a much weaker model. Our proposed algorithm is fault-tolerant and supports the collision-free movement of the robots. It successfully assembles robots on the left and right boundaries of the region.

Musical robot swarms, timing, and equilibria

This paper studies swarms of autonomous musical robots and its contributions are twofold. First, I introduce Dr. Squiggles, a simple rhythmic musical robot, which serves as a general platform for studying human-robot and robot-robot musical interaction. Secondly, I use three Dr. Squiggles robots to study what happens when musical robots listen to, learn from, and respond to one another while improvising music together. This paper has a supplementary video at https://www.youtube.com/watch?v=yN711HXPfuY which shows the three robots playing some of the equilibrium rhythms.