A Survey and Analysis of Multi-Robot Coordination

International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

Efficient, collision-free multi-robot navigation in an environment abstraction framework

Industrial automation deploys a continuously increasing amount of mobile robots in favor of classical linear conveyor systems for material flow handling in manufacturing and intralogistics. This increases flexibility by handling a larger variety of goods, improves scalability by adapting the fleet size to varying system loads, and enhances fault tolerance by avoiding single points of failure. However, it also raises the need for efficient, collision-free multi-robot navigation. This core problem is first precisely modeled in a form that differs from existing approaches specifically in terms of application relevance and structured algorithmic treatability. Collision-free trajectories for the mobile robots between given start and goal locations are sought so that the number of goals reached per time is as high as possible. Based on this, a decoupled solution called the Collaborative Local Planning Framework (CLPF), is designed and implemented, which, in contrast to existing solutions, aims at avoiding deadlocks with the greatest possible concurrency. Moreover, this solution includes the handling of dynamic inputs consisting of both moving and non-moving robots. For testing, performance analysis, and optimization, due to the complexity of multi-robot systems, the use of simulation is common. However, this also creates a gap between real and simulated robots. These issues can be reduced by using several different simulators---albeit with the disadvantage of further increasing complexity. For this purpose, the Robot Experimentation Framework (REF) is introduced to write robotic experiments with a unified interface that can be run on multiple simulators and also on real hardware. It facilitates the creation of experiments for performance assessment, (parameter) optimization and runtime analysis. The framework has proven its effectiveness throughout this thesis. Lastly, experimental proof of the viability of the solution is provided based on a case study of a complete (simulated) assembly system of decentralized autonomous agents for the production of highly individualized automobiles. This integrates all developed concepts into a holistic application of industrial automation. Detailed evaluations of more than 800 000 solved scenarios with more than 5 700 000 processed goals have experimentally proven the robustness and reliability of the developed concepts. Robots have never crashed into each other in any of the conducted experiments, empirically proving the claimed safety guarantees. A fault-tolerance analysis of the decentralized assembly system has experimentally proven its resilience to failures at workstations and, thus, specifically revealed an advantage over linear conveyor systems

Role Engine Implementation for a Continuous and Collaborative Multi-Robot System

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multi-robot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian Process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment's skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach's feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man and Cybernetics(T-SMC

Hierarchical Traffic Management of Multi-AGV Systems With Deadlock Prevention Applied to Industrial Environments

This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the presented control architecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections,), a predefined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors. Note to Practitioners-This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the presented control architecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections, ), a predefined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors

Velocity field path-planning for single and multiple unmanned ariel vehicles

Unmanned aerial vehicles (UAV) have seen a rapid growth in utilisation for reconnaissance, mostly using single UAVs. However, future utilisation of UAVs for applications such as bistatic synthetic aperture radar and stereoscopic imaging, will require the use of multiple UAVs acting cooperatively to achieve mission goals. In addition, to de-skill the operation of UAVs for certain applications will require the migration of path-planning functions from the ground to the UAV. This paper details a computationally efficient algorithm to enable path-planning for single UAVs and to form and re-form UAV formations with active collision avoidance. The algorithm presented extends classical potential field methods used in other domains for the UAV path-planning problem. It is demonstrated that a range of tasks can be executed autonomously, allowing high level tasking of single and multiple UAVs in formation, with the formation commanded as a single entity

Development of an Industrial Internet of Things (IIoT) based Smart Robotic Warehouse Management System

According to data of Census and Statistics Department, freight transport and storage services contributed to 90% of the employment of logistics sector in the period from 2010 to 2014. Traditional warehouse operations in Hong Kong are labor-intensive without much automation. With the rapid increasing transaction volume through multi-channel, the preference for next-day delivery service has been increasing. As a result, 3rd party logistics providers have realized the importance of operational efficiency. With the advent of Industry 4.0 emerging technologies including Autonomous Robots, Industrial Internet of Things (IIoT), Cloud Computing, etc., a smart robotic warehouse management system is proposed as it redefines the warehouse put-away and picking operations from man-to-goods to goods-to-man using autonomous mobile robots. This paper aims to develop and implement an IIoT-based smart robotic warehouse system for managing goods and autonomous robots, as well as to make use of the autonomous mobile robots to deliver the goods automatically for put-away and picking operations. The significance of the paper is to leverage the Industry 4.0 emerging technologies to implement the concept of smart warehousing for better utilization of floor space and labor force so as to improve logistics operational efficiency

Route planning methods for a modular warehouse system

In this study, procedures are presented that can be used to determine the routes of the packages transported within a modular storage system. The problem is a variant of robot motion planning problem. The structures of the procedures are developed in three steps for the simultaneous movement of multiple unit-sized packages in a modular warehouse. The proposed heuristic methods consist of route planning, tagging, and main control components. In order to demonstrate the solution performance of the methods, various experiments were conducted with different data sets and the solution times and qualities of the proposed methods were compared with previous studies. It was found that the proposed methods provide better solutions when taking the number of steps and solution time into consideration