Coordinative object-transportation by multiple industrial mobile robots using coupler with mechanical compliance

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.

The paper is concerned with the region tracking control problem of multiple nonholonomic mobile robot systems with collision avoidance. The peculiar characteristic of the proposed scheme is that a class of potential energy functions is chosen to form the desired target region constraining the robots for aggregation, and some local potential energy functions are selected to maintain collision avoidance among robots. Such that each mobile robot only needs to interact with their neighbors to realize the region tracking control task. Moreover, this paper provides a new analytical framework of region tracking control for a network of wheeled mobile robots, in which the integration of the system decomposition and sliding mode control method enables to convert the nonholonomic constraint problem of the controlled mobile robot system into a holonomic constraint problem. This provides a new approach to solving the mobile robot control problem. Theoretical analysis and numerical simulation results are given to validate the effectiveness of the proposed control scheme.

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.

In this paper, a novel DRL algorithm based on a DQN is proposed for multiple mobile robots to find optimized paths. The multiple robots’ states are the inputs of the DQN. The DQN estimates the Q-value of the agents’ actions. After selecting the action with the maximum Q-value, the multiple robots’ actions are calculated and sent to them. Then, the robots will explore the area and detect the obstacles. In the area, there are static obstacles. The robots should detect the static obstacles using a LiDAR sensor. The other moving robots are recognized as dynamic obstacles that need to be avoided. The robots will give feedback on the reward and the robots’ new states. A positive reward will be given when a robot successfully arrives at its goal point. If it is in a free space, zero reward will be given. If the robot collides with a static obstacle or other robots or reaches its start point, it will receive a negative reward. Multiple robots explore safe paths to the goals at the same time, in order to improve learning efficiency. If a robot collides with an obstacle or other robots, it will stop and wait for the other robots to complete their exploration tasks. The episode will end when all robots find safe paths to reach their goals or when all of them have collisions. This collaborative behavior can reduce the risk of collisions between robots, enhance overall efficiency, and help avoid multiple robots attempting to navigate through the same unsafe path simultaneously. Moreover, storage space is used to store the optimal safe paths of all robots. Finally, the multi-robots will learn the policy to find the optimized paths to go to the goal points. The goal of the simulations and experiment is to make multiple robots efficiently and safely move to their goal points.

This paper presents an unified approach to the task space analysis of a wheeled mobile manipulator interacting with the environment. The system considered consists of two articulated manipulators atop a wheeled mobile platform handling a common object. We derive the task space ellipsoid, both kinematics and dynamics, for a wheeled mobile manipulator which takes into account manipulation and locomotion. The ellipsoid is able to visualize how the manipulator and the platform can contribute to a task execution by integrating the mobility of the platform with the manipulability of the arms as one unified measure. This measure can be useful not only for the task space analysis of a single mobile manipulator, but also for the coordination of multiple arms, mobile robots or mobile manipulators.

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 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.

Consensus problems of multiple nonholonomic mobile robots are considered in this paper. These problems are simplified into consensus problems of two subsystems based on structure of nonholonomic mobile robots. Linear distributed controllers are constructed respectively for these two subsystems thanks to the theory of nonautonomous cascaded systems. Consensus of multiple nonholonomic mobile robots has been realized using the methodology proposed in this paper no matter whether the group reference signal is persistent excitation or not. Different from previous research on cooperative control of nonholonomic mobile robots where the consensus problem under persistent exciting reference has received a lot of attention, this paper reports the first consensus result for multiple nonholonomic mobile robots whose group reference converges to zero. Simulation results using Matlab illustrate the effectiveness of the proposed controllers in this paper.

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 problem of fixed-time tracking control for multiple nonholonomic mobile robots systems is studied, where a camera with uncalibrated parameters is used to measure the status of each robot. By employing graph theory, fixed-time stability theory, adding a power integrator technique and Lyapunov stability analysis, a novel type of fixed-time controller is proposed for each mobile robot, and the control schemes can make the tracking errors of each robot converge to zero within fixed time. The convergence time obtained in this paper is independent of initial conditions of the whole system, this control method is more practical in some situations. In the end, some simulation examples are presented to illustrate the effectiveness of the proposed method.

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.

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

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.

A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot

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.