Cooperative sweeping by multiple mobile robots

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.

Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark en-titled Explore-Bench. The benchmark involves various explo-ration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep rein-forcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and 12 × speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Fur-thermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https://github.com/efc-robot/Explore-Bench.

Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control

Multiple mobile robots are a practical platform for realizing the concept of distributed manipulation. New approach for manipulating large number of objects using distributive actuators or agent has been suggested. Multiple mobile robots provide the ability to conduct actions simultaneously from various locations and directions. Using two mobile robots and flexible tool such as a piece of string or rope, a large number of objects that are placed at sparse locations can be sorted and grouped by single action. The Voronoi separation algorithm is proposed as a path generation method for preplanning cooperative actions of multiple robots. The sorting task is conducted on-line using fuzzy logic controller with preplanned subgoals. The proposed control framework was verified by an experiment using two autonomous mobile robots with a flexible tool attachment of fixed length.

This paper deals with multi-objective behavior coordination and sensory networks based on fuzzy control for multiple mobile robots. In multi-objective behavior coordination, weights are assigned to basic behaviors and updated gradually according to the time series of sensory inputs. A robot with our behavior coordination can take a smooth action. The sensory network is the self-update mechanism of a sensory range in order to realize flexible control with a small number of control rules. Both methods have been successfully applied to a single mobile robot. In this paper, we focus on an environment including multiple mobile robots. We examine the effects of the values for updating behavior weights and sensory ranges on the performance of multiple mobile robots. From the experimental results, we show the effectiveness of the multi-objective behavior coordination and sensory networks in the environment including multiple mobile robots.

This paper proposes an approach to the motion coordination of multiple industrial mobile robots (AGVs). The task of the robots is to cooperatively transport a common object. An ordinary mobile robot has uncertain dynamics, and its motion is constrained by a non-holonomic constraint. Furthermore, multiple robots with a common object form a closed-chain structure which would cause the severe dynamical interactions. The authors' approach overcomes the inherent problems by introducing a robot-mounted coupler with mechanical compliance which provides compliance between each robot and the object. The hierarchical coordinative control scheme is presented to perform accurate object-transportation based on the synchronous motions of the robots. Simulation and fundamental experiment by using a commercially available AGV demonstrate the effectiveness of the authors' approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This article, concerned with a part of the research on distributed coordination work by multiple robots, discusses an algorithm for creating maps of unknown environments which are searched for and observed by multiple mobile robots, and on the results of a simulation experiment using this algorithm. This algorithm comprises a moving method, an observation method, and a task planning method which are intended to help the multiple mobile robots carry out an efficient search of unknown environments.

This paper presents a way of collecting indoor RF propagation loss data utilizing multiple mobile robots collaborating through double-layered networks. The collected data are used to place wireless access points to maximize coverage with a minimum number of access points. A novel approach is used to navigate multiple robots using grid based maps which is a key issue in mobile robot utilization. Using grid based maps and range-finders to acquire surrounding information and match with a pre-populated information, robot locations are correctly identified. This resolved the problem of weak or no GPS signal in an indoor environment for position determination. The data acquisition strategy is studied without requiring a central source of timing and synchronization f or multiple robots. This is done using script-based robot command execution with exchange of request and response control messages. Lastly, numerical analysis of effects of navigation and data errors and their tolerances for proper interpretation of the collected data is presented.

The simple and labor-intensive tasks of workers on the job site are rapidly becoming digital. In the work environment of logistics warehouses and manufacturing plants, moving goods to a designated place is a typical labor-intensive task for workers. These tasks are rapidly undergoing digital transformation by leveraging mobile robots in automated warehouses. In this paper, we studied and tested realistically necessary conditions to operate mobile robots in an automated warehouse. In particular, considering conditions for operating multiple mobile robots in an automated warehouse, we added more complex actions and various routes and proposed a method for improving sparse reward problems when learning paths in a warehouse with reinforcement learning. Multi-Agent Reinforcement Learning (MARL) experiments were conducted with multiple mobile robots in an automated warehouse simulation environment, and it was confirmed that the proposed reward model method makes learning start earlier even there is a sparse reward problem and learning progress was maintained stably. We expect this study to help us understand the actual operation of mobile robots in an automated warehouse further.

In this paper, we propose a motion planning method for cooperative transportation of a large object by multiple mobile robots in a complicated environment. We must plan the motions of the object and mobile robots to avoid obstacles in a three-dimensional environment. A computation time to solve this problem is exploded, since we need search a solution in a high dimensional configuration space (C-space). Accordingly, we reduce the dimensions of C-space using the feature of the transportation by multiple mobile robots. In our method, all things (the object, the robots and the obstacles) are represented by an octree that is the approximate cell decomposition method for a three-dimensional environment. We can find a solution with A* search algorithm in this smaller dimensional C-space using the potential field. We verify the effectiveness of our proposed motion planning method through simulations.

An autonomous and decentralized robot system, ACTRESS, is being developed as an intelligent robot system which is composed of multiple robotic agents. In this paper, the basic concept of ACTRESS and a strategy for organizing multiple mobile robots including a communication framework in ACTRESS are introduced. The negotiation for distributed problem solving and cooperative actions is discussed. Especially, in order to manage and share the changing environment information, negotiation between multiple mobile robots and an environment manager is required. Finally, a wireless communication system for negotiation between multiple robotic agents is developed. Its performance is analyzed by experimental evaluation on searching for an environment manager by mobile robots, and some examples of communication including negotiation are shown. >

This paper deals with a framework of decentralized approach to division of labor by multiple mobile robots. An iterative-transportation task by multiple robots with multiple sets of starts (pick-up place of the object) and goals (put down place) is considered as the task. On each route between a start and a goal, the efficiency of transportation improves when the number of robots increases. Due to jams, however, excessive number of robots on the same route causes inefficiency. We propose a control law of each robot to choose an appropriate route so as to optimize the total efficiency of the transportation, where each robot utilizes information which can be obtained only by local observation (without any explicit communication among robots). The proposed control is based on the estimation of the number of robots on the routes in the future. In simulation, it was verified that the proposed control law realized 96% efficiency of the fully centralized control by appropriately choosing the route, compared with the case where global information can be utilized.

In this paper, a unique multiple mobile robots system is proposed to enable engineering students and engineers in the field to efficiently learn subsumption architecture and develop swarm intelligence. The subsumption architecture is known as one of the behaviour-based artificial intelligence. Each of multiple mobile robots within the system has three wheels driven by DC motors and six position sensitive detector (PSD) sensors. Network-based subsumption architecture is considered to realise a schooling behaviour by using only information from the PSD sensors. Further, a server supervisory control is introduced for poor hardware platforms with limitations of software development, i.e., the mobile robots can only behave based on the most simply subdivided reaction behaviours, i.e., reflex actions, generated from agents. Experimental results show interesting behaviour among the multiple mobile robots, such as following, avoidance and schooling.

We proposed a car transportation system using multiple mobile robots, iCART II (intelligent Cooperative Autonomous Robot Transporters type II), based on "a-robot-for-a-wheel" concept. In this system, multiple robots, MRWheel (a Mobile Robot for a Wheel), lift up all wheels of a car and transport it in coordination by using interaction forces. However inertial and friction forces applied to modules for lifting up wheels prevent MRWheels from transporting a car. Therefore, this paper proposes an estimation algorithm of the vehicle weight based on adaptive control. If MRWheels can estimate a transported vehicle weight, MRWheels will estimate and compensate the inertial and friction forces. At first we model lifting up a wheel by a MRWheel and derive adaptive control low. And the proposed algorithm is applied to a MRWheel and the experimental results confirm its validity.

Mobile robots are gradually entering our human spaces. Apart from being able to accurately localize themselves, in populated environments robots also need to detect humans and recognize their characteristics. In this work we focused on using 3D LiDARs for sensing, and in particular on the question whether they can be used to classify characteristics of people around the robot. Moreover, since using sensors from multiple robots is expected to give more accurate recognition, we compared several ways how to combine multiple 3D LiDARs. We evaluated these methods using simulator data as well as actual real-world data. For the real-world data we created a novel dataset taken with multiple robots equipped with 3D LiDARs in a shopping center, which also includes manually labeled characteristics of the detected pedestrians. The results show that combining several 3D LiDARs makes the recognition more accurate. However, we were not able to achieve satisfactory accuracy with real-world data.