Team Of Mobile Robots Research Articles

Temporal Logic Task Planning and Intermittent Connectivity Control of Mobile Robot Networks

In this paper, we develop a distributed intermittent communication and task planning framework for mobile robot teams. The goal of the robots is to accomplish complex tasks, captured by local linear temporal logic formulas, and share the collected information with all other robots and possibly also with a user. Specifically, we consider situations where the robot communication capabilities are not sufficient to form reliable and connected networks, while the robots move to accomplish their tasks. In this case, intermittent communication protocols are necessary that allow the robots to temporarily disconnect from the network in order to accomplish their tasks free of communication constraints. We assume that the robots can only communicate with each other when they meet at common locations in space. Our distributed control framework jointly determines local plans that allow all robots to fulfill their assigned temporal tasks, sequences of communication events that guarantee information exchange infinitely often, and optimal communication locations that minimize a desired distance metric. Simulation results verify the efficacy of the proposed controllers.

Decentralized Energy-Aware Co-Planning of Motion and Communication Strategies for Networked Mobile Robots

In this article, a decentralized planning scheme is proposed to determine simultaneously communication and motion strategies for a team of mobile robots. These robots accomplish a collection of target visiting tasks in a complex environment with optimal energy consumption and guaranteed end-to-end connectivity. Information generated during the team deployment is transmitted to an operation center via a multihop wireless network whose channels are modeled by stochastic variables. For each announced task, mobile robots adopt different roles depending on the task’s nature and the team’s current configuration; then, each robot determines its communication and motion policies by solving a convex optimization problem. Avoiding inter-robot collisions and obstacles is also taken into account. The suggested approach leads to the efficient use of available robots and their energy resources compared to the rival methods in the literature. Effectiveness of the proposed algorithm is illustrated by computer simulations.

Distributed assignment with limited communication for multi-robot multi-target tracking

We study the problem of tracking multiple moving targets using a team of mobile robots. Each robot has a set of motion primitives to choose from in order to collectively maximize the number of targets tracked or the total quality of tracking. Our focus is on scenarios where communication is limited and the robots have limited time to share information with their neighbors. As a result, we seek distributed algorithms that can find solutions in bounded amount of time. We present two algorithms: (1) a greedy algorithm that is guaranteed finds a $2$-approximation to the optimal (centralized) solution albeit requiring $|R|$ communication rounds in the worst-case, where $|R|$ denotes the number of robots; and (2) a local algorithm that finds a $\mathcal{O}\left((1+\epsilon)(1+1/h)\right)$-approximation algorithm in $\mathcal{O}(h\log 1/\epsilon)$ communication rounds. Here, $h$ and $\epsilon$ are parameters that allow the user to trade-off the solution quality with communication time. In addition to theoretical results, we present empirical evaluation including comparisons with centralized optimal solutions.

Energy-aware planning of motion and communication strategies for networked mobile robots

This paper investigates the co-planning problem of motion and communication strategies for a team of networked mobile robots. The robotic team that has limited energy budget should accomplish specified tasks in the workspace. The aim is to co-design the spatial trajectories of the robots to visit the goal positions, and the communication variables to assure continuous information exchange in the presence of wireless channels’ uncertainties. The motion and communication policies are determined by solving a convex optimization problem such that the end-to-end communication rates between the mobile robots and the operation center stay above the given levels with a specified probability. Avoiding inter-robot collisions and optimal consumption of communication and motion energies are also considered in the planning procedure. Comparative simulations’ results are presented to demonstrate that the proposed approach is able to navigate the robots towards their goals in complex environments, while notable conservation in the energy resources is achieved.

Leader-Follower Formation System of Multi-Mobile Robots for Gas Source Searching

Currently, robots are used to help dangerous human activities, including monitoring and searching for sources of toxic and flammable gases. One of the characteristics of a gas leak is that the direction of the gas changes easily due to wind. Therefore, when using a mobile robot, it will easily lose the direction of the gas plume which results in the longer time needed to find the gas source. In this study, the method of searching for gas source has been developed with the leader-follower formation system. This formation aims to make the multi-mobile robots not easily lose the direction of the gas plume. A simple Particle Swarm Optimization (PSO) algorithm is used to configure V-shaped formation and keep the distance among the robots. The XBee module is used to transmit the data and measure the distance of each robot using the Received Signal Strength Indicator (RSSI) value. The Fuzzy Logic Control (FLC) method is applied to mobile robot navigation to track the gas plume. The PSO algorithm and the FLC method are implemented in the Arduino Mega 2560 microcontroller. The experimental results show that the mobile robot team can maintain a distance of 1 to 2 meters between each robot.

Coordination of nonholonomic mobile robots for diffusive threat defense

This paper studies coordination of a team of nonholonomic mobile robots with smart actuators for defending against invasive threat to a planar convex area. The threat refers to a kind of harmful substance such as chemical pollutant appearing outside and moving towards the area. The invasion of threat can be modeled by a 2D unsteady reaction-diffusion process. To reflect the adverse effect of threat on the area, a so-called risk intensity field is introduced. The value of risk intensity is equal to the concentration of threat measured by a static mesh sensor network. Based on this risk intensity field, a coordination control scenario using Voronoi tessellation is formulated. In order to minimize the actuator performance loss and reduce the total average risk intensity simultaneously, a generalized centroidal Voronoi tessellation (CVT) algorithm including optimal motion control and risk mitigation control is designed. The proposed algorithm is gradient-based and guides mobile robots to track their optimal trajectories asymptotically. Meanwhile, two conditions of choosing control gains are derived to keep the total average risk intensity below a safety level. Several simulation examples with different cases of threat invasion are provided and the advantage of proposed algorithm over traditional control method is presented.

High-Gain Observer-Based Neural Adaptive Feedback Linearizing Control of a Team of Wheeled Mobile Robots

SummaryThis paper addresses the neural network (NN) output feedback formation tracking control of nonholonomic wheeled mobile robots (WMRs) with limited voltage input. A desired formation is achieved based on the leader–follower strategy utilizing hyperbolic tangent saturation functions to reduce the risk of actuator saturation. The controller is developed by incorporating the high-gain observer and radial basis function (RBF) NNs using the inverse dynamics control technique. The high-gain observer is introduced to estimate velocities of the followers. The RBF NN preserves the robustness of the proposed controller against uncertain nonlinearities. The adaptive laws are also combined by a robust control term to estimate the weights of RBF NN, approximation errors, and bounds of unknown time-variant environmental disturbances. A Lyapunov-based stability analysis proves that all signals of the closed-loop system are bounded, and tracking errors are uniformly ultimately bounded. Finally, some simulations are carried out to show the effectiveness of the proposed controller for a number of WMRs.

Adaptive Sampling and Reduced-Order Modeling of Dynamic Processes by Robot Teams

This letter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic process. We propose a distributed strategy, where robots collect sparse sensor measurements, create a reduced-order model of a spatio-temporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. The key contributions of this process are twofold: first, leveraging the dynamics of the process of interest to determine where to sample and how to estimate the process; and second, maintaining fully distributed models, sensor measurements, and estimates of the time-varying process. We illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We also test our approach with physical marine robots sampling a process in a water tank.

Distributed multi-target search and tracking using the PHD filter

This paper proposes a distributed estimation and control algorithm that enables a team of mobile robots to search for and track an unknown number of targets. These targets may be stationary or moving, and the number of targets may vary over time as targets enter and leave the area of interest. The robots are equipped with sensors that have a finite field of view and may experience false negative and false positive detections. The robots use a novel, distributed formulation of the Probability Hypothesis Density (PHD) filter, which accounts for the limitations of the sensors, to estimate the number of targets and the positions of the targets. The robots then use Lloyd’s algorithm, a distributed control algorithm that has been shown to be effective for coverage and search tasks, to drive their motion within the environment. We utilize the output of the PHD filter as the importance weighting function within Lloyd’s algorithm. This causes the robots to be drawn towards areas that are likely to contain targets. We demonstrate the efficacy of our proposed algorithm, including comparisons to a coverage-based controller with a uniform importance weighting function, through an extensive series of simulated experiments. These experiments show teams of 10–100 robots successfully tracking 10–50 targets in both 2D and 3D environments.

Smart Sensors Applications for a New Paradigm of a Production Line.

Industrial plants are going to face a deep renewing process within the Industry 4.0 scenario. New paradigms of production lines are foreseen in the very near future, characterized by a strict collaboration between humans and robots and by a high degree of flexibility. Such envisaged improvements will require the smart use of proper sensors at very different levels. This paper investigates three different aspects of this industrial renewing process, based on three different ways of exploiting sensors, toward a new paradigm of a production line. The provided contributions, offering various types of innovation and integration, are relative to: (i) a virtual sensor approach for manual guidance, increasing the potentialities of a standard industrial manipulator, (ii) a smart manufacturing solution to assist the operator’s activity in manual assembly stations, through an original exploitation of multiple sensors, and (iii) the development of an advanced robotic architecture for a flexible production line, in which a team of autonomous mobile robots acts as a meta-sensor net supporting traditional automated guided vehicles. Accurate analyses of existing state-of-the-art solutions compared with the proposed ones are offered for the considered issues.

Fault-tolerant multi-agent scheme for leader/follower formation control of homogeneous mobile robot team

This paper deals with fault-tolerance and formation control problems for homogeneous mobile robots under leader/follower failures by using the multi-agent paradigm. These issues are addressed by a cooperative approach supported by two kinds of agents: 1) robot agents (classified into a leader and a set of followers depending on their objective function), assigned to robots for collecting information, making decisions and transferring them to physical robots for execution; 2) supervisory agent (an interface between the control system and the team), synchronises and coordinates tasks of the other agents. These agents interact by a negotiation protocol to satisfy their objectives: 1) preserve predefined leader/follower formation; 2) conserve robots order inside the formation; 3) manage dynamic disturbance events (e.g., unexpected breakdown, integration of new robots). To validate this scheme and evaluate its performances, different simulations are examined by using ten robots evolving inside a grid environment cluttered with static obstacles with/without failures.

Fault-tolerant multi-agent scheme for leader/follower formation control of homogeneous mobile robot team

This paper deals with fault-tolerance and formation control problems for homogeneous mobile robots under leader/follower failures by using the multi-agent paradigm. These issues are addressed by a cooperative approach supported by two kinds of agents: 1) robot agents (classified into a leader and a set of followers depending on their objective function), assigned to robots for collecting information, making decisions and transferring them to physical robots for execution; 2) supervisory agent (an interface between the control system and the team), synchronises and coordinates tasks of the other agents. These agents interact by a negotiation protocol to satisfy their objectives: 1) preserve predefined leader/follower formation; 2) conserve robots order inside the formation; 3) manage dynamic disturbance events (e.g., unexpected breakdown, integration of new robots). To validate this scheme and evaluate its performances, different simulations are examined by using ten robots evolving inside a grid environment cluttered with static obstacles with/without failures.

A Hybrid Decentralized Coordinated Approach for Multi-Robot Exploration Task

Abstract Exploring the environment using multi-robot systems is a fundamental process that most automated applications depend on. This paper presents a hybrid decentralized task assignment approach based on Partially Observable Semi-Markov Decision Processes called HDec-POSMDPs, which are general models for multi-robot coordination and exploration problems in which robots can make their own decisions according to its local data with limited communication between the robot team. In this paper, a variety of multi-robot exploration algorithms and their comparison have been tackled. These algorithms, which have been taken into consideration, are dependent on different parameters. Collectively, there are five metrics maximize the total exploration percentage, minimize overall mission time, reduce the number of hops in the networked robots, reduce the energy consumed by each robot and minimize the number of turns in the path from the start pose cells to the target cells. Therefore, a team of identical mobile robots is used to perform coordination and exploration process in an unknown cell-based environment. The performance of the task depends on the strategy of coordination among the robots involved in the team. Therefore, the proposed approach is implemented, tested and evaluated in MRESim computer simulator, and its performance is compared with different coordinated exploration strategies for different environments and different team sizes. The experimental results demonstrate a good performance of the proposed approach compared to the four existing approaches.

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams.

Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.

Multi-robot replication of ant collective towing behaviours.

In this work, teams of small mobile robots are used to test hypotheses about cooperative transport by ants. This study attempts to explain a decrease in steady-state transport speed with increasing team size that was previously observed in the ant Novomessor cockerelli. Two models of one-dimensional collective towing are compared: one in which transporters with different maximum speeds pull the payload with continuous, variable forces and another in which transporters with identical speeds pull with intermittent, unsynchronized forces. A statistical analysis of ant data supports the hypothesis that ants behave according to the first model, in which the steady-state transport speed is the maximum speed of the slowest teammate. By contrast, the ant data are not consistent with the second model, which predicts constant speed regardless of team size. To verify these predictions, the ant behaviours in each model are translated into decentralized controllers and implemented on teams of two to four robots. The controller for the first model incorporates a real-time reinforcement learning algorithm that successfully reproduces the observed relationship between ant team size and transport speed. The controller for the second model yields the predicted invariance of transport speed with team size. These results show the value of robotic swarms for testing mechanistic hypotheses about biological collectives.

Cooperative Robot Exploration Strategy Using an Efficient Backtracking Method for Multiple Robots

This paper presents a cooperative robot exploration (CRE) strategy that is based on the sensor-based random tree (SRT) star method. The CRE strategy is utilized for a team of mobile robots equipped with range finding sensors. Existing backtracking techniques for frontier-based (FB) exploration involve moving back thorough the previous position where the robot has passed before. However, in some cases, the robot generates inefficient detours to move back to the position that contains frontier areas. In an effort to improve upon movement and energy efficiencies, this paper proposes the use of a hub node that has a frontier arc; thereby, the robots backtrack more directly to hub nodes by using the objective function. Furthermore, each robot cooperatively explores the workspace utilizing the data structure from the entire team of robots, which consists of configuration data and frontier data. Comparative simulations of the proposed algorithm and the existing SRT-star algorithm are implemented and described. The experiment is presented to demonstrate the application of the proposed strategy in real-time. Utilizing the proposed algorithm and exploration strategy, the results indicate that a team of robots can work more efficiently by reducing the distance of exploration and the number of node visited.

Neuro-Dominating set scheme for a fast and efficient robot deployment in internet of robotic things

Internet of Robotic Things (IoRT) is a new concept introduced for the first time by ABI Research. Unlike the Internet of Things (IoT), IoRT provides an active sensorization and is considered as the new evolution of IoT. In this context, we propose a Neuro-Dominating Set algorithm (NDS) to efficiently deploy a team of mobile wireless robots in an IoRT scenario, in order to reach a desired inter-robot distance, while maintaining global connectivity in the whole network. We use the term Neuro-Dominating Set to describe our approach, since it is inspired by both neural network and dominating set principles. With NDS algorithm, a robot adopts different behaviors according whether it is a dominating or a dominated robot. Our main goal is to show and demonstrate the beneficial effect of using different behaviors in the IoRT concept. The obtained results show that the proposed method outperforms an existing related technique (i.e., the Virtual Angular Force approach) and the neural network based approach presented in our previous work. As an objective, we aim to decrease the overall traveled distance and keep a low energy consumption level, while maintaining network connectivity and an acceptable convergence time.

Large-scale 3D printing by a team of mobile robots

Abstract Scalability is a problem common to most existing 3D printing processes, where the size of the design is strictly constrained by the chamber volume of the 3D printer. This issue is more pronounced in the building and construction industry, where it is impractical to have printers that are larger than actual buildings. One workaround consists in printing smaller pieces, which can then be assembled on-site. This workaround generates however additional design and process complexities, as well as creates potential weaknesses at the assembly interfaces. In this paper, we propose a 3D printing system that employs multiple mobile robots printing concurrently a large, single-piece, structure. We present our system in detail, and report simulation and experimental results. To our knowledge, this is the first physical demonstration of large-scale, concurrent, 3D printing of a concrete structure by multiple mobile robots.

Distributed multi-robot formation control in dynamic environments

This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.

Area Coverage Optimization Using Heterogeneous Robots: Algorithm and Implementation

This paper advances the previous theoretical work of authors by conducting experiments on a generalized coverage optimization algorithm using a team of heterogeneous mobile robots. A scalar measure (herein called the density) of the environment defines a nonuniform coverage metric of the area. Mobile robots are spatially configured such that their asymptotic placements in an area maximize the nonuniform coverage metric. Over the last fifteen years, a large body of research work has been conducted in solving the area coverage optimization problem where the focus was mainly on theoretical results followed by mostly numerical simulations. In some cases, the coverage optimization algorithms were validated with experimental results, but the implementation platforms were suitable to specific homogeneous robot platforms. Here, the emphasis is on the real-time implementation of the authors’ previously published theoretical results on area coverage optimization problems using a team of heterogeneous mobile robots. The robots are heterogeneous in the sense that they have different actuator limits, physical dimensions, and processing capabilities. The modularity of the algorithm stems from the fact that the additional hardware/software architecture is open-source and can be applied to different robots regardless of their internal electromechanical system architectures. The algorithm is implemented using the emerging robot operating system in a multithreaded manner. A commercial robot simulator was used to validate the coverage performance followed by a set of experiments conducted in an indoor environment.