Team Of Autonomous Mobile Robots Research Articles

Interoperable communication for autonomous mobile robots

The deployment of teams of autonomous mobile robots provides potential by leveraging the strengths of individual robots and compensating for their disadvantages. This makes them particularly well suited to carrying out complex scenarios for logistics, environmental monitoring or search and rescue. However, the success of such teams relies on achieving seamless interoperability among differing robots. In the area of the Internet of Robotic Things (IoRT), addressing this interoperability challenge becomes paramount. Therefore, an interoperable communication concept is introduced that considers interoperability based on semantic, syntactic, and technical clusters. It is designed to meet the specific requirements of multimodal autonomous mobile robots. Based on legal considerations, this method enables the integration and collaboration of different autonomous mobile robots in real-life applications. A logistics scenario involving multiple autonomous mobile robots illustrates the applicability of this interoperable communication concept.

A Step-by-Step Algorithm for Finding the Optimal Strategy for the Behavior of a Group of Robots

The solution of the multi-criteria problem, which includes the distribution of objectives, the planning of trajectories and the optimization of energy consumption, is considered in the realization of the collective interaction of robots. It is proposed to use a genetic algorithm according to the chosen conditions (constraints) and optimality criteria to find the best strategy for group behavior. The considerable difficulty in choosing how to control a team of autonomous mobile robots represents the distribution of tasks among agents that operate under conditions of parametric and information uncertainty, possess modest hardware, power and functionality. Therefore, the implementation of a multi-stage search for an optimal solution requires a specialized approach that takes into account the whole range of dynamic parameters, allowing for real-time target correction and degradation of robots until they fail. The basis of the proposed neurogenetic algorithm is a new algorithm for calculating the fitness function, in which the results of the neural network method of trajectory planning for a group of robots are used, as well as information about the initial charge of the batteries of robotic agents of the collective, the energy consumption of each agent and the preliminary estimation of the energy required by some agent to perform the individual tasks available to it. In order to ensure an acceptable performance of the algorithm and given the high dynamism of the external environment, it was decided to limit the search for solutions to only one step (the next working beat of the collective). The paper presents the results of the simulation of the task of finding the optimal behavior of robots, the algorithm of calculation of the specialized fitness function and the options of step-by-step search of the global strategy of distribution of tasks, which make it possible to increase the efficiency of the use of the team of robots due to the guaranteed production of the result while minimizing the total time of completion of all the tasks, as well as to increase the working time of the team due to the correct energy consumption.

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.

Coordination strategies for multi-robot exploration and mapping

Situational awareness in rescue operations can be provided by teams of autonomous mobile robots. Human operators are required to teleoperate the current generation of mobile robots for such applications; however, teleoperation is increasingly difficult as the number of robots is expanded. As the number of robots is increased, each robot may also interfere with one another and eventually decrease mapping performance. As presented here, through careful consideration of robot team coordination and exploration strategy, large numbers of mobile robots can be allocated to accomplish the mapping task more quickly and accurately. We present both the coordination and exploration strategies and present results from experiments in simulation as well as with up to nine mobile platforms.

Distributed multi-robot patrol: A scalable and fault-tolerant framework

This paper addresses the Multi-Robot Patrolling Problem, where agents must coordinate their actions while continuously deciding which place to move next after clearing their locations. This problem is commonly addressed using centralized planners with global knowledge and/or calculating a priori routes for all robots before the beginning of the mission. In this work, two distributed techniques to solve the problem are proposed. These are motivated by the need to adapt to the changes in the system at any time and the possibility to add or remove patrolling agents (e.g., due to faults).The first technique presented is greedy and aims to maximize robot’s local gain. The second one is an extension of the former, which takes into account the distribution of agents in the space to reduce interference and foster scalability.The validation of the proposed solution is preliminarily conducted through realistic simulations as well as experiments with robot platforms in a small lab scenario. Subsequently, the work is verified in a large indoor real-world environment with a team of autonomous mobile robots with scalability and fault-tolerance assessment.

A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the Particle Swarm Optimization (PSO) model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.

Attractor dynamics approach to formation control: theory and application

In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.

A multiagent cooperative mobile robotics approach for search and rescue missions

This paper presents a reusable framework for coordinating a team of mobile robots that can accomplish high level or tightly coupled missions, which could not be easily achieved using single robot solutions. To complete this task, we consider the behavior-based architecture model for the basic control layer. For coordinating the team of autonomous mobile robots, we use the agent-oriented software engineering paradigm. We apply the combination of a single and of multiagent techniques for evaluating a search-and-rescue mission. The experiments are using the Pioneer 2 DX robot and the Saphira simulator.