Multi-robot Systems Research Articles

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

A distributed algorithm for the multi-robot minimum time task allocation problem

Purpose The purpose of this study is to address the challenge of task allocation in multi-robot systems by getting the minimum overall task completion time and task allocation scheme while also minimizing robot energy consumption. This study aims to move away from traditional centralized methods and validate a more scalable distributed approach. Design/methodology/approach This paper proposes a distributed algorithm for the multi-robot task allocation problem, aimed at getting the minimum task completion time along with the task allocation scheme. The algorithm operates based on local interaction information rather than global information. By using the Consensus-Based Auction Algorithm (CBAA), it seeks to effectively minimize energy consumption without affecting the minimum completion time required for overall task allocation. Findings The proposed distributed algorithm successfully reduces robot energy consumption while effectively obtaining the shortest overall task completion time and corresponding task allocation scheme. Numerical simulations conducted using MATLAB software demonstrated its superior performance, and empirical testing on the Turtlebot3-Burger robot platform further substantiated these findings. Originality/value The original contribution of this study lies in the development of an enhanced distributed task allocation strategy using CBAA to improve efficiency in multi-robot environments. Its value extends to applications that require rapid and resource-aware coordination, such as automated logistics or search-and-rescue operations.

Advancing Multi-Robot Path Planning through Nature Inspired Algorithms

In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.

PF-MAAC: A learning-based method for probabilistic optimization in time-constrained non-adversarial moving target search

This paper investigates the multi-robot efficient search (MuRES) problem with a focus on maximizing the probability of capturing a moving target within a predefined time constraint. Given the complexity of the MuRES problem, traditional optimization algorithms often result in significant computational overhead. As a result, learning-assisted intelligent optimization, particularly reinforcement learning (RL), has emerged as a prominent research trend, providing more efficient and adaptive solutions. However, the non-additive nature of the objective to maximize capture probability complicates the direct application of canonical RL-based algorithms. To address the challenge, we propose the probabilistically factorized multi-agent actor-critic (PF-MAAC) algorithm, which serves as a lightweight solution aligned with probability theory specifically designed to handle the complexities of the maximal capture probability objective. PF-MAAC is composed of (1) a generalized temporal difference (GTD) module to establish the temporal-difference relationship of the central value function, (2) a probability-based factorization (P-FAC) module to decompose the central value function into individual ones in a probability-compliant manner, and (3) an extended policy gradient (EPG) module which updates each robot’s actor-network based on the decomposed individual value function. Comparative simulations across various MuRES test environments demonstrate that PF-MAAC outperforms state-of-the-art methods. Furthermore, we successfully deployed PF-MAAC in a real multi-robot system for moving target search in a self-constructed indoor environment, achieving the satisfactory results for different time constraints.

Global Round-up Strategy Based on an Improved Hungarian Algorithm for Multi-robot Systems

In this paper, a round-up strategy is proposed to optimize global target selection and improve the efficiency of multi-robot round-up behavior, which is applicable to the round-up situation with multiple pursuers and multiple evaders. Firstly, a constrained pursuer control strategy is designed to maintain the effectiveness of the area-minimizing round-up strategy. Additionally, a novel and detailed procedure is presented to make the area-minimizing round-up strategy based on Voronoi easier to understand. Then, an improved Hungarian algorithm-based global optimization strategy for target selection is proposed. This algorithm aims to reduce the efficiency due to the uneven position distribution of the robots. Finally, experimental results are given to demonstrate the proposed strategy can improve the global efficiency of multi-robot round-up.

Teleoperation system for multiple robots with intuitive hand recognition interface

Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human–machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human–machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human–machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw.

Adaptive Time-Varying Formation Maneuvering Control for Multi-Robot Systems in Complex Obstacle-Rich Environments

To tackle the challenge of time-varying formation control for underactuated robots under model parameter uncertainties and environmental disturbances, this study proposes an affine formation control approach enhanced by an Extended State Observer. Initially, using affine positioning theory and polynomial interpolation, guidelines for selecting leader vehicles and trajectory planning methods are established, whereby the trajectory of follower vehicles is uniquely determined through the stress matrix. To address the cumulative disturbances arising from model uncertainties and environmental factors impacting the formation, an Extended State Observer with optimized parameters is introduced. Furthermore, a distributed affine formation control method with disturbance rejection is designed specifically for underactuated robots with “nonlinear, strongly coupled” dynamics, ensuring that the formation system can track the target configuration within bounded error margins. Finally, theoretical analysis and simulation outcomes ultimately confirm the efficacy of the control approach in achieving resilient formation tracking.

Multi-robot task allocation for optional tasks with hidden workload: Using a model-based hyper-heuristic strategy

Multi-robot task allocation (MRTA) is a classical problem in multi-robot systems. This paper analyzes the situations where the objective of the robots is to minimize the time cost of completing a certain proportion of tasks instead of completing all tasks, i.e., the tasks are optional. Besides, in this problem, the true workload of each task is initially hidden and can only be known after the preliminary workload is completed. As the tasks are optional, selecting a suitable combination of the tasks is quite important. The main challenge in this problem is that the robots cannot exactly select the tasks that are easy to complete because the true workload is hidden. In previous similar problems (i.e., MRTA with incomplete information), reallocation-based method is a general method. However, in this problem, if the robots exactly reallocate the tasks after collecting enough information, the sunk costs (i.e., the tasks that have been partially performed but are not selected in reallocation) limit the decrease of the time cost. Thus, we design a new hyper-heuristic strategy. In detail, a simple heuristic method that lets the robots appropriately give up some allocated tasks is combined with reallocation to design the low-level heuristic (LLH). The high-level strategy (HLS) seeks the optimal setting of LLH based on a meta-heuristic algorithm 11We respectively test particle swarm optimization (PSO) and simulated annealing (SA).. The strategies are tested based on various random instances, and the hyper-heuristic strategy can outperform the benchmark strategies in most instances. In some instances, the maximum improvement of results led by the hyper-heuristic is more than 9%.

A Distributed Competitive and Collaborative Coordination for Multirobot Systems

A Distributed Competitive and Collaborative Coordination for Multirobot Systems

Simultaneous Position and Orientation Planning of Nonholonomic Multirobot Systems: A Dynamic Vector Field Approach

Simultaneous Position and Orientation Planning of Nonholonomic Multirobot Systems: A Dynamic Vector Field Approach

Heterogeneous multi-robot system mission planning with cooperative replenishment through data-driven rendezvous point selection

AbstractWith advances in autonomy technology, the use of multi-robot systems is becoming increasingly viable and efficient. In particular, heterogeneous systems are effective in complicated missions that require various capabilities. When the missions are prolonged, certain robots such as quadcopter-typed unmanned aerial vehicles (UAVs) run out of energy and require replenishment. As some robots such as unmanned surface vessels (USVs) have large payload capacities for carrying supplies, the UAVs can receive the necessary resources from the USVs amid the missions. In this paper, we propose a mission planning framework that aims to minimize the total mission duration with consideration of the energy replenishment of robots and the heterogeneity of robots and tasks. The proposed framework consists of four components: task allocation, rendezvous point selection, task planning, and plan combination. For replenishment, the location of the rendezvous must be chosen, and we propose using a data-driven approach to predict the best rendezvous point. Then, the prediction result is used for computing candidate plans of each robot in a distributed fashion, and the best candidates are combined to compute the final plan. To validate the efficacy of the proposed method, simulated experiments of various problem configurations are performed and analyzed.

A Framework for Simultaneous Task Allocation and Planning under Uncertainty

We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties, such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure.

A Lightweight, Centralized, Collaborative, Truncated Signed Distance Function-Based Dense Simultaneous Localization and Mapping System for Multiple Mobile Vehicles.

Simultaneous Localization And Mapping (SLAM) algorithms play a critical role in autonomous exploration tasks requiring mobile robots to autonomously explore and gather information in unknown or hazardous environments where human access may be difficult or dangerous. However, due to the resource-constrained nature of mobile robots, they are hindered from performing long-term and large-scale tasks. In this paper, we propose an efficient multi-robot dense SLAM system that utilizes a centralized structure to alleviate the computational and memory burdens on the agents (i.e. mobile robots). To enable real-time dense mapping of the agent, we design a lightweight and accurate dense mapping method. On the server, to find correct loop closure inliers, we design a novel loop closure detection method based on both visual and dense geometric information. To correct the drifted poses of the agents, we integrate the dense geometric information along with the trajectory information into a multi-robot pose graph optimization problem. Experiments based on pre-recorded datasets have demonstrated our system's efficiency and accuracy. Real-world online deployment of our system on the mobile vehicles achieved a dense mapping update rate of ∼14 frames per second (fps), a onboard mapping RAM usage of ∼3.4%, and a bandwidth usage of ∼302 KB/s with a Jetson Xavier NX.

Intelligent multi-robot collaborative transport system

The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

Fuzzy task assignment in heterogeneous distributed multi-robot system

This study addresses the problem of coordination in cooperative multi-robot systems performing complex tasks. An analysis of cooperative behavior in mobile multi-robot systems in terms of task execution accuracy by heterogeneous robots is carried out. In addition, we evaluate the capacity and compatibility of tasks assigned to robots to optimize task execution without using direct communication with the robots or a central decision-making unit. A model for task selection in heterogeneous distributed multi-robot systems is proposed. It is based on two processes: the first decomposes complex tasks into elementary tasks, and the second assigns elementary tasks to mobile robots for real-time execution. The distribution of elementary tasks is NP-hard, which leads us to recommend approximate solutions. A fuzzy system called Fuzzy Decision Making in Task Selection is proposed, which uses fuzzy logic to solve this problem. This system allows robots to choose to perform any task in the future. An approach is presented that uses two cascading fuzzy systems. The first calculates the utility of the robot and then activates the second fuzzy system to calculate the utility of the task. By using the output of the fuzzy decision system in our model, each robot will be able to decide for itself which tasks to perform. The results of a simulation of mobile robots transporting goods demonstrate the effectiveness of this fuzzy decision-maker.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Formation Control of Nonholonomic Multirobot Systems Over Robot Coordinate Frames and Its Application to LiDAR-Based Robots

Formation Control of Nonholonomic Multirobot Systems Over Robot Coordinate Frames and Its Application to LiDAR-Based Robots

CE-MRS: Contrastive Explanations for Multi-Robot Systems

CE-MRS: Contrastive Explanations for Multi-Robot Systems

Fixed-Time Command Filter Fuzzy Adaptive Formation Control for Nonholonomic Multirobot Systems With Unknown Dead-Zones

Fixed-Time Command Filter Fuzzy Adaptive Formation Control for Nonholonomic Multirobot Systems With Unknown Dead-Zones

Safety-Guaranteed Distributed Formation Control of Multi-Robot Systems Over Graphs With Rigid and Elastic Edges

Safety-Guaranteed Distributed Formation Control of Multi-Robot Systems Over Graphs With Rigid and Elastic Edges