Multi‐robot Mapping Using Omnidirectional‐Vision SLAM Based on Fisheye Images

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional‐vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas–Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi‐robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional‐vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.     

Global localization of multirobot formations using ceiling vision SLAM strategy

Global localization is an important matter in multirobot formations, but the issue has not been sufficiently studied yet. In this paper, we successfully extend the single robot ceiling vision SLAM to multirobot formations for addressing global localization problem. Each robot is equipped with a monocular camera that looks upward to the ceiling. The monocular camera system used for ceiling observation appears to be more convenient than other active sensors such as laser and panoramic camera. A public global map shared by every robot is developed for positioning update. Two global localization strategies are proposed. The first strategy is to globally localize one robot only and then localize the others based on the relative poses amongst the robots. The second strategy is to globally localize all the robots simultaneously. The former requires less computational resource, and the later exhibits better localization performance. A feature-based matching approach is utilized to calculate the relative poses amongst the robots. Simulation experiments are finally performed to demonstrate the effectiveness of the proposed approach.     

Merging Occupancy Grid Maps From Multiple Robots

Mapping can potentially be speeded up in a significant way by using multiple robots exploring different parts of the environment. But the core question of multirobot mapping is how to integrate the data of the different robots into a single global map. A significant amount of research exists in the area of multirobot mapping that deals with techniques to estimate the relative robots poses at the start or during the mapping process. With map merging, the robots in contrast individually build local maps without any knowledge about their relative positions. The goal is then to identify regions of overlap at which the local maps can be joined together. A concrete approach to this idea is presented in form of a special similarity metric and a stochastic search algorithm. Given two maps m and m', the search algorithm transforms m' by rotations and translations to find a maximum overlap between m and m'. In doing so, the heuristic similarity metric guides the search algorithm toward optimal solutions. Results from experiments with up to six robots are presented based on simulated as well as real-world map data.     

Control framework for collaborative robot using imitation learning-based teleoperation from human digital twin to robot digital twin

Despite the deployment of collaborative robots for various industrial processes, their teaching and control remain comparatively difficult tasks compared with general industrial robots. Various imitation learning methods involving the transfer of human poses to a collaborative robot have been proposed. However, most of these methods depend heavily on deep learning-based human recognition algorithms that fail to recognize complicated human poses. To address this issue, we propose an automated/semi-automated vision-based teleoperation framework using human digital twin and a collaborative robot digital twin models. First, a human pose is recognized and reasoned to a human skeleton model using a convolution encoder-decoder architecture. Next, the developed human digital twin model is taught using the skeletons. As human and collaborative robots have different joints and rotation architectures, pose mapping is achieved using the proposed Bezier curve-based smooth approximation. Then, a real collaborative robot is controlled using the developed robot digital twin. Furthermore, the proposed framework works successfully using a human digital twin in the case of recognition failures of human poses. To verify the effectiveness of the proposed framework, transfers of several human poses to a real collaborative robot are tested and analyzed.     

Autonomous decentralized control for formation of multiple mobile robots considering ability of robot

The purpose of this study is to control multiple mobile robots in formation considering the ability of a robot using the "Leader-Following" strategy. There are three features of this study. First, a performance index that shows mobile robot ability is quantified. Specifically, maximum acceleration and maximum velocity of a robot are defined by maximum admissible rotation and maximum continuous torque of a motor. The performance index is quantified from arrival time on the destination using these parameters. Second, a new controller is proposed based on the performance index, so that robots can be controlled according to robot ability. Third, a compliance controller using a virtual repulsion is suggested in this paper, so that each robot can avoid collision. Finally, simulation and experiments are done in a real-time system using RT-Messenger. RT-Messenger allows robots to transmit information regarding their positions to each other in real time. These results shows the validity of the proposed method.     

Complete coverage navigation of cleaning robots using triangular-cell-based map

This paper presents a novel approach for navigation of cleaning robots in an unknown workspace. To do so, we propose a new map representation method as well as a complete coverage navigation method. First, we discuss a triangular cell map representation which makes the cleaning robot navigate with a shorter path and increased flexibility than a rectangular cell map representation. Then, we propose the complete coverage navigation and map construction methods which enable the cleaning robot to navigate the complete workspace without complete information about the environment. Finally, we evaluate the performance of our proposed triangular cell map via the existing distance-transform-based path-planning method comparing it to that of the rectangular cell map. Also, we verify the effectiveness of the proposed methods through computer simulations.     

Navigation Behavior Selection Using Generalized Stochastic Petri Nets for a Service Robot

Appropriate design and control of behaviors of mobile robots are important for their successful autonomous navigation in a real dynamic environment. This paper proposes a formal selection framework of multiple navigation behaviors for a service robot. In the presented approach, modeling, analysis, and performance evaluation are carried out based on generalized stochastic Petri nets (GSPNs). By adopting a probabilistic approach, the proposed framework helps the robot to select the most desirable navigation behavior in run time according to environmental conditions. Moreover, after mission completion, the robot evaluates its prior navigation performance from accumulated data, and automatically uses the results to improve its future operations. Also, GSPNs have several advantages over direct use of other modeling formalisms such as finite state automata (FSA) or Markov processes (MPs). We conduct experiments on real guidance tasks with visitors by implementing the framework in the guide robot Jinny at the National Science Museum of Korea. The results show that the proposed strategy is useful for a robot's selection of an appropriate navigation behavior in a dynamic environment.     

Enhance Energy Conservation Based on Residual Energy and Distance for WSNs

As the advancements in the field of artificial intelligence technologies continue to grow, robots are being built by the researchers as an attempt to render services to the people. In this regard, the robots can communicate effectively with the people and be able to complete all the tasks adequately given to them. These service robots while being developed requires the dialogue services to be developed to interact effectively with human beings providing far better user experience. Thus, the robot been built can provide domain-specific knowledge as well as able to provide consultations in various domains. We in this paper adopted a service-oriented approach for developing context-aware communication services for the cloud robot. The proposed work aims at training the context-aware model developed. The context-aware model is responsible for answering the questions put forward by the users and possess the ability to exploit the answers corresponding to the questions presented. An integrated framework is used to combine the contextual information and communication services. The performance of the proposed model can be evaluated based on Inverse Rank Mean (IRM). Evolutionary testing methods are used for testing the data in the proposed model. The results thus obtained shows the effectiveness of the proposed approach.

     

移动送料机器人地图构建与运动控制研究

移动机器人的地图构建与运动控制是智能移动机器人进行自主导航的基础。针对工业装配生产线的不同工位需要送料的需求,本文以一台轮式差速移动送料机器人为研究对象,利用激光雷达作为传感器,搭建一个装配生产线,设计、实现了基于ROS的移动送料机器人的地图构建与运动控制策略。利用slamgmapping算法包,实现了机器人在特定装配生产线环境下的同时定位与地图构建;在主程序代码中利用状态机的机制,实现了机器人到达指定工位的运动控制。实验结果表明,该算法和运动控制策略可以满足在所搭建的装配生产线中进行定位和运行。     

A cooperative multi-robot architecture for moving a paralyzed robot

In this paper we study the possibility of cooperation between reactive and deliberative based robots, which are generally considered as antinomic approaches. We focus on a case study composed, on the one hand, of an “intelligent” robot that submits failures which prevent it from moving, and on the other hand, of a pool of simple autonomous mobile robots which are able to push. The paralyzed robot can broadcast signals to recruit mobile robots and to be pushed by them. These signals are interpreted as force fields by agents in order to compute their reactive behavior. We present these different robot behaviors and analyse two experiments. We show that the proposed system provides a control loop which is independent of the number of robots pushing on each arm, showing that a combination of multi-agent and deliberative architectures can define intelligent and robust multi-robot systems.     

An end-to-end transmission architecture for the remote control of robots over IP networks

Teleoperation via the Internet is attracting more and more attention from both academia and industry. Physical interaction with remote environments over IP networks, however, poses many technical challenges that are still outstanding, such as time delay, limited bandwidth, and unreliable transmission. To deal with these problems, the majority of current work is concentrated on developing advanced remote control algorithms or interface techniques whereas data transmission between the human operator and the remote robot through the Internet is often treated as a given condition. This paper describes a novel data transmission architecture, for which the core is the trinomial transport protocol, to facilitate the remote control of Internet robots. The trinomial protocol is analyzed theoretically and its advantages over other existing protocols are verified by simulation and experimental studies.     

Mobile robot localization: integrating measurements from atime-of-flight laser

This paper presents an algorithm for environment mapping by integrating scans from a time-of-flight laser and odometer readings from a mobile robot. The range weighted Hough transform (RWHT) is used as a robust method to extract lines from the range data. The resulting peaks in the RWHT are used as feature coordinates when these lines/walls are used as landmarks during navigation. The associations between observations over the time sequence are made in a systematic way using a decision directed classifier. Natural geometrical landmarks are described in the robot frame together with a covariance matrix representing the spatial uncertainty. The map is thus built up incrementally as the robot moves. If the map is given in advance, the robot can find its location and navigate relative to this a priori given map. Experimental results are presented for a mobile robot with a scanning range measuring laser having 2-cm resolution. The algorithm was also used for an autonomous plastering robot on a construction site. The sensor fusion algorithm makes few erroneous associations     

A cooperative optimization strategy for distributed multi-robot manipulation with obstacle avoidance and internal performance maximization

Reactive multi-robot manipulation can be of great assistance in many applications. In this article a distributed cooperation strategy for object manipulation with multiple robots in un-modelled environments is proposed to tackle workpiece transportation and dynamic obstacle avoidance simultaneously. A decentralised optimisation process will run first to generate robot pose references and then a joint-level control is responsible for tracking. Since in multi-arm manipulation systems the available workspace for each robot is more restricted, necessary internal performance optimisation is also included in the planning phase. A velocity-level optimum will be solved and then transformed to a position-level control reference so that it can be combined with many compliant interactive controllers such as impedance control. Although no torque or force sensor is required and each robot only has access to local information, when avoiding obstacles the coordination strategy can generate synchronised group motion so that internal forces are avoided. Only task-space variables will be communicated, therefore it is easy to team up different types of robots, making the system more flexible. Simulation and experiments with two redundant manipulators have been done to validate the proposed design, and data show that the online computation speed is fast.     

The Conro modules for reconfigurable robots

The goal of the Conro Project is to build deployable modular robots that can reconfigure into different shapes such as snakes or hexapods. Each Conro module is, itself, a robot and hence a Conro robot is actually a multirobot system. In this paper we present an overview of the Conro modules, the design approach, an overview of the mechanical and electrical systems and a discussion on size versus power requirement of the module. Each module is self-contained; it has its own processor, power supply, communication system, sensors and actuators. The modules, although self-contained, were designed to work in groups, as part of a large modular robot. We conclude the paper by describing some of the robots that we have built using the Conro modules and describing the miniature custom-made Conro camera as an example of the type of sensors that can be carried as payload by these robots.     

Adaptive action selection without explicit communication formultirobot box-pushing

The paper describes a novel action selection method for multiple mobile robots box-pushing in a dynamic environment. The robots are designed to need no explicit communication and be adaptive to dynamic environments by changing modules of behavior. The various control methods for a multirobot system have been studied both in centralized and decentralized approaches, however, they needed explicit communication such as a radio, though such communication is expensive and unstable. Furthermore, though it is a significant issue to develop adaptive action selection for a multirobot system to a dynamic environment, few studies have been done on it. Thus, we propose action selection without explicit communication for multirobot box-pushing which changes a suitable behavior set depending on a situation for adaptation to a dynamic environment. First, four situations are defined with two parameters: the existence of other robots and the task difficulty. Next, we propose an architecture of action selection which consists of a situation recognizer and sets of suitable behaviors to the situations and carefully design the suitable behaviors for each of the situations. Using the architecture, a mobile robot recognizes the current situation and activates the suitable behavior set to it. Then it acts with a behavior-based approach using the activated behaviors and can change the current situation when the environment changes. We fully implement our method on four real mobile robots and conduct various experiments in dynamic environments. As a result, we find out our approach is promising for designing adaptive multirobot box-pushing     

Combined kinematic and dynamic control of vehicle-manipulator systems

A vehicle-manipulator system (VMS) is a class of mobile robots characterised by their ability to carry or be a robotic arm and therefore also manipulate objects. The VMS class includes vehicles with a robotic manipulator, free-floating space robots, aerial manipulators and underwater vehicle-manipulator systems (UVMSs). All of these systems need a kinematic controller to solve the kinematic redundancy of the VMS and a dynamic controller to follow the reference given by the kinematic controller. In this paper, we propose a combined kinematic and dynamic control approach for VMSs. The approach uses the singularity-robust multiple task-priority (SRMTP) framework to generate a velocity reference combined with a dynamic velocity controller based on a robust sliding mode controller (SMC). Any SMC can be used as long as it can make the velocity vector converge to the velocity reference vector in finite time. This novel approach allows us to analyse the stability properties of the kinematic and dynamic subsystems together in the presence of model uncertainty. We show that the multiple set-point regulation tasks will converge asymptotically to zero without the strict requirement that the velocities are perfectly controlled. This novel approach thus avoids the assumption of perfect dynamic control that is common in kinematic stability analyses for robot manipulators. We present two examples of SMCs that can make the velocity vector converge to the velocity reference vector in finite time. We also demonstrate the applicability of the proposed approach through a simulation study of an articulated intervention-AUV (AIAUV), which is a type of UVMS, by conducting three simultaneous tasks. The results show that both SMC algorithms can make all the regulation tasks converge to their respective set-points. In the simulation study, we also include the results from two standard control methods, a proportional-integral-derivative (PID) controller and a feedback linearisation controller, and we use two different AIAUVs to illustrate the advantages and robustness achieved from using SMC.     

Rapid obstacle sensing using mobile robot sonar

This paper introduces rapid obstacle sensing using mobile robot sonar (ROSUM) a new method of applying simultaneous ultrasonic firing in environmental scanning. ROSUM focuses on treating undesirable crosstalk data with a heuristic pattern classification technique. In addition, least square curve fitting is applied to estimate bearing angles to the echoes’ reflecting points which partially eliminates the problem of sonar’s poor directionality. ROSUM enables full panorama scanning to be performed more rapidly then error eliminating rapid ultrasonic firing (EERUF), currently the most rapid method available. This makes ROSUM suitable for fast mobile robots navigating in dynamic environments since the system can detect obstacles which suddenly appear in the robot’s path, and which may not be detected by the low-frequency scans in time for the robot to safely avoid them. Criteria for sonar ring design enabling multi-wave superposition to attain a quasi-homogenous sonar beam intensity pattern are also outlined. ROSUM has been implemented and tested in a specular office-like environment using a mobile robot. As a result, obstacle detection was performed not only faster, but also more accurately in comparison to conventional methods.     

Mobile Robot Exploration in Indoor Environment Using Topological Structure with Invisible Barcodes

This paper addresses the localization and navigation problem in the movement of service robots by using invisible two dimensional barcodes on the floor. Compared with other methods using natural or artificial landmarks, the proposed localization method has great advantages in cost and appearance since the location of the robot is perfectly known using the barcode information after mapping is finished. We also propose a navigation algorithm which uses a topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls, and many static obstacles. The proposed algorithm also has the advantage that errors which occur in each node are mutually independent and can be compensated exactly after some navigation using barcodes. Simulation and experimental results were performed to verify the algorithm in the barcode environment, showing excellent performance results. After mapping, it is also possible to solve the kidnapped robot problem and to generate paths using topological information.