Application Of Multi-robot Systems Research Articles

A review of graph-based multi-agent pathfinding solvers: From classical to beyond classical

Multi-agent pathfinding (MAPF) is a well-studied abstract model for navigation in a multi-robot system, where every robot finds the path to its goal position without any collision. Due to its numerous practical applications of multi-robot systems, MAPF has steadily emerged as a research hotspot. The optimal solution for MAPF is NP-hard. In this paper, we offer a comprehensive analysis of different MAPF solvers. First, we review the cutting-edge solvers of classical MAPF, including optimal, bounded sub-optimal, and unbounded sub-optimal. The performance of some representative classical MAPF solvers is quantitatively compared. In the next part, we summarize the beyond classical MAPF solvers, which try to use the classical MAPF solvers in real-world scenarios. Last, we conclude some challenges that MAPF is experiencing in detail, review recent research on these issues, and make some suggestions for further work.

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation.

Within the scope of the ongoing efforts to fight climate change, the application of multi-robot systems to environmental mapping and monitoring missions is a prominent approach aimed at increasing exploration efficiency. However, the application of such systems to gas sensing missions has yet to be extensively explored and presents some unique challenges, mainly due to the hard-to-sense and expensive-to-model nature of gas dispersion. For this paper, we explored the application of a multi-robot system composed of rotary-winged nano aerial vehicles to a gas sensing mission. We qualitatively and quantitatively analyzed the interference between different robots and the effect on their sensing performance. We then assessed this effect, by deploying several algorithms for 3D gas sensing with increasing levels of coordination in a state-of-the-art wind tunnel facility. The results show that multi-robot gas sensing missions can be robust against documented interference and degradation in their sensing performance. We additionally highlight the competitiveness of multi-robot strategies in gas source location performance with tight mission time constraints.

Mission specification and decomposition for multi-robot systems

Service robots are increasingly being used to perform missions comprising dangerous or tedious tasks previously executed by humans. However, their users—who know the environment and requirements for these missions—have limited or no robotics experience. As such, they often find the process of allocating concrete tasks to each robot within a multi-robot system (MRS) very challenging. Our paper introduces a framework for Multi-Robot mission Specification and decomposition (MutRoSe) that simplifies and automates key activities of this process. To that end, MutRoSe allows an MRS mission designer to define all relevant aspects of a mission and its environment in a high-level specification language that accounts for the variability of real-world scenarios, the dependencies between task instances, and the reusability of task libraries. Additionally, MutRoSe automates the decomposition of MRS missions defined in this language into task instances, which can then be allocated to specific robots for execution—with all task dependencies appropriately taken into account. We illustrate the application of MutRoSe and show its effectiveness for four missions taken from a recently published repository of MRS applications.

Multi-robot Coordination for Environment Exploration and Patrol

With the development of manufacturing, people benefit from using multiple mobile robots to complete tasks in various scenarios, and multi-robot planning coordination has become a hot research topic in recent decades. This paper introduces two popular multi-robot system applications: multi-robot exploration and multi-robot patrol. First, the two kinds of problems are introduced and compared. Then, typical methods to figure out the problems are presented, including the frontier-based and market-based methods for exploration and the v-Min algorithm and v-Neighbor algorithm for patrol. Finally, this paper gives the experiment results and conclusions of different methods.

CARA: Connectivity-Aware Relay Algorithm for Multi-Robot Expeditions

The exploration of unknown environments is an essential application of multi-robot systems, particularly in critical missions, such as hazard detection and search and rescue. These missions share the need to reach full coverage of the explorable space in the shortest time possible. To minimize the completion time, robots in the fleet must be able to reliably exchange information about the environment with one another. One of the main methods to expand coverage is by placing relays. Existing relay-placement algorithms tend to either require prior knowledge of the environment, or they rely on maintaining specific distances between the relays and the rest of the robots. These approaches lack flexibility and adaptability to the environment. This paper introduces the "Connectivity-Aware Relay Algorithm" (CARA), a dynamic context-aware relay-placement algorithm that does not require any prior knowledge of the environment. We compare CARA against a state-of-the-art distance-based relay-placement algorithm. Our results demonstrate that CARA outperformed the state-of-the-art algorithm in terms of the time to completion by a factor of 10 as it placed, on average, half the number of relays.

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications.

The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances.

Multirobot Control Strategies for Collective Transport

One potential application of multirobot systems is collective transport, a task in which multiple robots collaboratively move a payload that is too large or heavy for a single robot. In this review, we highlight a variety of control strategies for collective transport that have been developed over the past three decades. We characterize the problem scenarios that have been addressed in terms of the control objective, the robot platform and its interaction with the payload, and the robots’ capabilities and information about the payload and environment. We categorize the control strategies according to whether their sensing, computation, and communication functions are performed by a centralized supervisor or specialized robot or autonomously by the robots. We provide an overview of progress toward control strategies that can be implemented on robots with expanded autonomous functionality in uncertain environments using limited information, and we suggest directions for future work on developing such controllers.

Image style transfer for autonomous multi-robot systems

In deep learning, multi-robot systems intelligently cooperate to perform a complex task, and improve the performance of a single robot through communication, collaboration and sharing of information. Intelligent collaborative work makes the research and application of multi-robot systems develop rapidly. The traditional image style transfer algorithm mainly uses mathematical modeling to characterize the texture of the picture, and combines the content image with the style image to achieve the effect of style transfer. This style transfer algorithm ignores the edge distribution of the image, which makes the contour of the generated image blurred, and it takes a long time in the iterative process of style transfer, and the effect is poor. Therefore, this paper proposes a ceramic decoration pattern style migration algorithm based on the ESPCN model. The algorithm uses the Laplace operator to sharpen the image to highlight the edge distribution, and then uses downsampling to generate low-resolution images to reduce the iteration time of image style transfer. The final generated image uses ESPCN super-resolution reconstruction to reconstruct the low-resolution image. Converted to high-resolution images. The experimental results show that multiple image evaluation indicators show that the edge distribution of the generated image is clear, the iteration time is shortened, and the image quality and definition are improved.

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making using Full Consistency Method

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.

Exploration Strategies of Coordinated Multi-Robot System: A Comparative Study

Environment Exploration is the basic process that most of Multi Robot Systems applications depend on it. The exploration process performance depends on the coordination strategy between the robots participating in the team. In this paper the coordination of Multi Robot Systems in the exploration process is surveyed, and the performance of different Multi Robot Systems exploration strategies is contrasted and analyzed for different environments and different team sizes.

Robust Tracking of Soccer Robots Using Random Finite Sets

Maintaining a good estimation of the other robots’ positions is crucial in soccer robotics, as in most multirobot systems applications. Classical approaches use a vector representation of the robots’ positions and Bayesian filters to propagate them over time. However, these approaches suffer from the data association problem. To tackle this issue, this article presents a new methodology for the robust tracking of robots based on the Random Finite Sets framework, which doesn’t require any explicit data association. Moreover, the proposed methodology is able to integrate information shared by teammate robots, their positions, and their estimations of the other robots’ positions. The robots’ tracking is based on the use of a GM-PHD filter, where the estimations of the robots’ positions and observations are represented using mixture of Gaussians, but instead of associating a robot’s hypothesis or an observation to a given Gaussian, the weight of each Gaussian maintains an estimation of the number of robots that it represents. The methodology is validated in several soccer matches and compared with a classical multihypothesis EKF tracking methodology. The proposed method is able to reduce the errors of the estimated robots’ positions in about 35 percent.

EXPERIENCED TASK-BASED MULTI ROBOT TASK ALLOCATION

In multi robot system applications, it is possible that the robots transform their past experiences into useful information which will be used for next task allocation processes by using learning-based task allocation mechanisms. The major disadvantages of multi-robot Q-learning algorithm are huge learning space and computational cost due to generalized state and joint action spaces of robots. In this study, experienced task-based multi robot task allocation approach is proposed. According to this approach, robots believe to be experienced about the tasks most frequently done. Robots prefer to do these tasks rather than the inexperienced ones. Then, robots refuse to execute inexperienced tasks over time. This means that the system has reduced learning space. The proposed approach plays a crucial role to achieve required system performance and provides effective solutions to learning space dimensions. The effectiveness of the proposed algorithm is demonstrated by simulations on multi-robot task allocation problem.

Reliable Communication Protocol for Applications in Multi-Robot Systems

Multi-robot systems (MRSs) have a wide variety of applications, such as search and rescue in disaster scenarios, where many robots coordinate with each other to accomplish a task. Such MRS applications use infrastructureless environment in which robots rely on inherently unreliable ad hoc network to communicate with each other. Reliable communication among the peers can greatly enhance the performance of a multi-robot system. This paper proposes a reliable communication protocol (RCP) for applications in multi-robot systems. RCP acts as an interface between MRS applications and the underlying communication framework. RCP accepts data from MRS applications and reliably delivers it to other peers. RCP is transparent to MRS applications as well as the underlying communication hardware. To evaluate its performance, we have implemented RCP on seven Stargate micro-controllers that communicate with each other using an ad hoc network. Further, to test the performance of RCP in MRS applications involving higher number of peers, we have also implemented RCP on laptops with Intel i7 microprocessors. The obtained results show that RCP achieves reliability while reducing packet delivery time as well as the number of retries needed to deliver a failed packet.

Implementation of Coordinated Complex Dynamic Behaviors in Multirobot Systems

Decentralized control strategies for multirobot systems have been extensively studied over the past few years. Typically, these strategies aim at exploiting local interaction rules to regulate the overall state of the multirobot system toward a desired configuration, thus generating some desired coordinated behaviors, such as synchronization, swarming, deployment, or formation control. However, when considering the real-world application of multirobot systems, more complex cooperative dynamic behaviors are desirable. Along these lines, in this paper, we propose a methodology to control a multirobot system for cooperatively tracking arbitrarily defined periodic setpoint trajectories. This objective is fulfilled partitioning the multirobot system into independent robots (that can provide control inputs) and dependent robots (that are controlled through local interaction). The motion of the independent robots is then defined in such a way that, exploiting local interactions, the dependent robots are controlled to track the desired trajectories. The proposed control strategy is validated by means of simulations and experiments on real robots.

Bridging the reality gap between the Webots simulator and e-puck robots

This work presents a set of techniques to improve the cross-compatibility between the Webots simulator and the educational e-puck mobile robot. The work is motivated by the need of a realistic simulation environment, especially designed to consider the unique challenges associated to multi-robot systems (MRS), such as the robot–robot interaction (RRI). This article starts by proposing a new Webots programming architecture that provides full control of the e-puck’s firmware. By doing so, one can easily improve the already existing e-puck’s functionalities on Webots, as well as adding new functionalities required by most MRS applications. Among the range of new possible functionalities, this article describes the successful implementation of the microphone, the speaker, and the Bluetooth for robot–robot communication. Due to the limitations of the e-puck’s Bluetooth modules, this work goes even further by bringing some insights about the Gumstix Overo COM turret and how robot–robot communication may be achieved using WiFi technology. The outcomes provided by the experiments conducted using both virtual and real e-pucks under the Webots framework open new doors for low-cost multi-robot applications.

A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation

This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time.

Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time

This work deals with the application of multi-robot systems to real tasks and, in particular, their coordination through interaction based control systems. Within this field, the practical solutions that have been implemented in real robots mainly use strongly coordinated architectures and assignment strategies because of reliability and fault tolerance issues when addressing problems in reality. Emergent approaches have also been proposed with limited success, basically due to the unpredictability of the behaviors obtained. Here, an emergent approach, called r-ASiCo, is presented containing a procedure to produce predictable solutions and thus avoiding the typical problems associated with these techniques. The r-ASico algorithm is the real time version of the Asynchronous Situated Co-evolution algorithm (ASiCo), which exploits natural open-ended evolution to generate emergent complex collective behaviors and deals with systems made up of a huge number of elements and nonlinear interactions. The goal of r-ASiCo is to design the global behavior desired for the robot team as a collective entity and allow the emergence of behaviors through the interaction of the team members using social rules they learn to implement. To this end, r-ASiCo manages a series of features that are inherent to natural evolution based methods such as energy exchange and mating selection procedures, together with a technique to guide the evolution towards a design objective, the principled evaluation function selection procedure. Hence, this paper presents the components and operation of r-ASiCo and illustrates its application through a collective cleaning task example. It was implemented using 8 e-puck robots in two different real scenarios and its results complemented with those of a 30 e-puck case. The results show the capabilities of r-ASiCo to create a self-organized and adaptive multi-robot system configuration that is tolerant to environmental changes and to failures within the robot team.