Particle Filter for Collaborative Multi-Robot Localization Tolerant to Recognition Error

Statistical algorithms using particle filters have been proposed previously for collaborative multi-robot localization. In these algorithms, by synchronizing each robot's belief or exchanging the particles of the robots, fast and accurate localization is attained. However, there algorithms assume correct recognition of other robots and the effects of recognition error are not considered. If the recognition of other robots is incorrect, a large amount of error in localization can occur. This paper describes this problem. Furthermore, in order to cope with the problem, an algorithm for collaborative multi-robot localization is proposed. In the proposed algorithm, the particles of a robot are exchanged with those of other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Received particles from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method copes with recognition error by using the remaining particles, and increases the accuracy of estimation by twice evaluating the exchanged particles of the sending and receiving robots. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.     

Elasto-geometrical error and gravity model calibration of an industrial robot using the same optimized configuration set

Position error is a significant limitation for industrial robots in high-precision machining and manufacturing. Efficient error measurement and compensation for robots equipped with end-effectors are difficult in industrial environments. This paper proposes a robot calibration method based on an elasto–geometrical error and gravity model. Firstly, a geometric error model was established based on the D-H method, and the gravity and compliance error models were constructed to predict the elastic deformation caused by the self-weight of the robot. Subsequently, the position error model was established by considering the attitude error of the robot flange coordinate system. A two-step robot configuration selection method was developed based on the sequential floating forward selection algorithm to optimize the robot configuration for calibrating the position error and gravity models. Then, the geometric error and compliance coefficient were identified simultaneously based on the hybrid evolution algorithm. The gravity model parameters were identified based on the same algorithm using the joint torque signal provided by the robot controller. Finally, calibration and compensation experiments were conducted on a KR-160 industrial robot equipped with a spindle using a laser tracker and internal robot data. The experimental results show that the robot tool center point error can be significantly improved by using the proposed method.     

Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of optimal assignment algorithms, and we expect this to be a fruitful area for future research.     

A generic controller architecture for intelligent robotic systems

This paper describes a research program to develop a novel reference architecture and design philosophy for an advanced robot controller for a new generation of robots. It is suggested that a new approach to robot controller design is required in order to bring the present generation of industrial robots in line with current and foreseeable technological developments. The paper also describes a prototype controller that has been developed using this design philosophy, and its deployment on an experimental robot. Practical results are presented from a series of investigations undertaken to illustrate the performance of the controller.     

Transferring knowledge across robots: A risk sensitive approach

One of the most impressive characteristics of human perception is its domain adaptation capability. Humans can recognize objects and places simply by transferring knowledge from their past experience. Inspired by that, current research in robotics is addressing a great challenge: building robots able to sense and interpret the surrounding world by reusing information previously collected, gathered by other robots or obtained from the web. But, how can a robot automatically understand what is useful among a large amount of information and perform knowledge transfer? In this paper we address the domain adaptation problem in the context of visual place recognition. We consider the scenario where a robot equipped with a monocular camera explores a new environment. In this situation traditional approaches based on supervised learning perform poorly, as no annotated data are provided in the new environment and the models learned from data collected in other places are inappropriate due to the large variability of visual information. To overcome these problems we introduce a novel transfer learning approach. With our algorithm the robot is given only some training data (annotated images collected in different environments by other robots) and is able to decide whether, and how much, this knowledge is useful in the current scenario. At the base of our approach there is a transfer risk measure which quantifies the similarity between the given and the new visual data. To improve the performance, we also extend our framework to take into account multiple visual cues. Our experiments on three publicly available datasets demonstrate the effectiveness of the proposed approach.     

Bridging the gap between feature- and grid-based SLAM

One important design decision for the development of autonomously navigating mobile robots is the choice of the representation of the environment. This includes the question of which type of features should be used, or whether a dense representation such as occupancy grid maps is more appropriate. In this paper, we present an approach which performs SLAM using multiple representations of the environment simultaneously. It uses reinforcement to learn when to switch to an alternative representation method depending on the current observation. This allows the robot to update its pose and map estimate based on the representation that models the surrounding of the robot in the best way. The approach has been implemented on a real robot and evaluated in scenarios, in which a robot has to navigate in- and outdoors and therefore switches between a landmark-based representation and a dense grid map. In practical experiments, we demonstrate that our approach allows a robot to robustly map environments which cannot be adequately modeled by either of the individual representations.     

Image-based fuzzy trajectory tracking control for four-wheel steered mobile robots

A four-wheel steered mobile robot is fit for a higher power or improvement in the movement speed of a robot than a two-independent wheeled one. Since a steered mobile robot that slips very often cannot apply a popular dead-reckoning method using rotary encoders, it is desirable to use external sensors such as cameras. This paper describes a method to trace a straight line for four-wheel steered mobile robots using an image-based control method. Its controller is designed as a fuzzy controller and evaluated through some simulations and real robot.     

Cleaning robot navigation using panoramic views and particle clouds as landmarks

The paper describes a visual method for the navigation of autonomous floor-cleaning robots. The method constructs a topological map with metrical information where place nodes are characterized by panoramic images and by particle clouds representing position estimates. Current image and position estimate of the robot are interrelated to landmark images and position estimates stored in the map nodes through a holistic visual homing method which provides bearing and orientation estimates. Based on these estimates, a position estimate of the robot is updated by a particle filter. The robot’s position estimates are used to guide the robot along parallel, meandering lanes and are also assigned to newly created map nodes which later serve as landmarks. Computer simulations and robot experiments confirm that the robot position estimate obtained by this method is sufficiently accurate to keep the robot on parallel lanes, even in the presence of large random and systematic odometry errors. This ensures an efficient cleaning behavior with almost complete coverage of a rectangular area and only small repeated coverage. Furthermore, the topological-metrical map can be used to completely cover rooms or apartments by multiple meander parts.     

An interactive programming system for the IBM 7545 robot

Industrial robots may be programmed using teach methods, off-line programming languages or by using interactive robot programming systems. This paper briefly explains each method, describes the advantages of developing interactive robot programming systems, and then describes an interactive robot programming system developed for the IBM 7545 robot. The approach used in the development process, the interactive execution and user options, and a demonstration of the operation of this interactive robot programming system are also presented.     

Development of CAM system based on industrial robotic servo controller without using robot language

This paper describes the development of a robotic CAM system for an articulated industrial robot RV1A from the view point of robotic servo controller. It is defined here that the CAM system includes an important function which allows an industrial robot to move along cutter location data (CL data) consisting of position and orientation components. In addition, the developed CAM system has a high applicability to other industrial robots whose servo systems are technically opened to end-users. The developed robotic CAM system works as a straightforward interface between a general CAD/CAM and an industrial robot. At the present stage, the relationship between CAD/CAM and industrial robots is not well established compared to NC machine tools that are widely spread in manufacturing industries. The CAM systems for NC machine tools are already established, however, the CAM system for industrial robots has not been sufficiently considered and developed yet. A teaching pendant is generally used to obtain position and orientation data of the arm tip before an industrial robot works. Here, in order to enhance the relationship between a conventional CAD/CAM system and an industrial robot, a simple and straightforward CAM system without using any robot language is developed and implemented. The basic design of the robotic CAM system and the experimental results are presented in this paper.     

Planning exploration strategies for simultaneous localization and mapping

In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map.We also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead.The planning algorithms which have been developed operate using simple but flexible models of the robot sensors and actuator abilities. Techniques that allow implementation of these sensor models on top of the capabilities of actual sensors have been provided.All of the proposed algorithms have been implemented either on real robots (for the case of individual robots) or in simulation (for the case of multiple robots), and experimental results are given.     

A collaborative localization tolerant to recognition error by double-check particle exchange

Statistical algorithms using particle filters for collaborative multi-robot localization have been proposed. In these algorithms, by synchronizing every robot’s belief or exchanging particles of the robots with each other, fast and accurate localization is attained. These algorithms assume correct recognition of other robots, and the effects of recognition errors are not discussed. However, if the recognition of other robots is incorrect, a large amount of error in localization can occur. This article describes this problem. Furthermore, an algorithm for collaborative multi-robot localization is proposed in order to cope with this problem. In the proposed algorithm, the particles of a robot are sent to other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Particles received from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method is tolerant to recognition error by the remaining particles and evaluating the exchanged particles in the sending and receiving robots twice, and if there is no recognition error, the proposed method increases the accuracy of the estimation by these two evaluations. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.     

Application of Localization Based on the DC Magnetic Field that Occurs in the Environment on Wheel-Type Mobile Agricultural Robots

A wheel-type mobile robot is simply able to localize with odometry. However, for mobile agricultural robots, it is necessary to consider that the environment is uneven terrain. Therefore, odometry is unreliable and it is necessary to augment the odometry by measuring the position of the robot relative to known objects in the environments. This paper describes the application of localization based on the DC magnetic field that occurs in the environment on mobile agricultural robots. In this research, a magnetic sensor is applied to scan the DC magnetic field to build a magnetic database. The robot localizes by matching magnetic sensor readings against the magnetic database. The experimental results indicate that the robot is able to localize accurately with the proposed method and the cumulative error can be eliminated by applying the localization results to compensate for the odometry.     

Towards cooperation of heterogeneous,autonomous robots: A case study of humanoid and wheeled robots

In this paper a case study of the cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended, compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.     

A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.     

A heterogeneous modular robotic design for fast response to a diversity of tasks

This paper describes a heterogeneous modular robot system design which attempts to give a quick solution to a diversity of tasks. The approach is based on the use of an inventory of three types of modules i.e., power and control module, joint module and specialized module. Each module type aims to balance versatility and functionality. Their design permits rapid and cost effective design and fabrication. They are interchangeable in different ways to form different robot or system configurations. Depending on the task, the operator decides what type of robot can provide the best performance within the mission. A spherical joint module is described and used to build different robots, hence, forward and inverse kinematics models are obtained. Finally, from the modules described in this work, several robot configurations such as robotic arms, leg-based robots and wheel-based robots are assembled to demonstrate the execution of manipulation and locomotion tasks.     

Multi-robot coordination for jams in congested systems

This paper describes a novel coordination methodology of autonomous mobile robots for jams in a congested system with bottlenecks. This methodology consists of two approaches based on an interaction force and behavior regulation rule for a robot. The former is for directly controlling velocity of a robot in the behavioral dynamics, and the latter is for amplifying the interaction force so that velocity of a robot is externally reduced in a certain place. In the first approach, a previously-proposed robot behavior control technique by the authors that utilizes the interaction force among robots is improved, and it enables the robots to reduce their velocity in response to not only a jam but also a decelerating robot immediately in front of them. In the second approach, a behavior regulation rule in connection with the interaction force is designed and provided in congested segments on a lane. Thus, the amplified interaction force causes the robots to move more slowly in the congested segments. The improved robot behavior control technique and behavior regulation rule are implemented in simulation experiments and compared to the previous robot behavior control technique and adaptive cruise control (ACC) that has been proposed for vehicles. Furthermore, the improved interaction force and behavior regulation rule are appended to ACC, and the potential of using ACC with the two approaches is discussed. Finally, the effectiveness of the improved interaction force and the behavior regulation rule for multi-robot coordination in a congested system with bottlenecks is shown.     

Navigation of Mobile Robots Using a Fuzzy Logic Controller

This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.     

A STEP-compliant Industrial Robot Data Model for robot off-line programming systems

Recently, various robot off-line programming systems have promoted their own robot data models, resulting in a plethora of robot representation methods and unchangeable data files among CAx and robot off-line programming systems. The current paper represents a STEP-compliant Industrial Robot Data Model (IRDM) for data exchange between CAx systems and robot off-line programming systems. Using this novel representation method, most resources involved in a robot manufacturing system can be represented. The geometric and mathematic aspects of industrial robots have been defined in IRDM, so that the robot off-line programming system could have abundant information to represent robots’ kinematic and dynamic behaviors. In order to validate the proposed models and approaches, a prototype robot off-line programming system with 3D virtual environment is presented. The functionalities of IRDM not only have significant meaning for providing a unified data platform for robot simulation systems, but also have the potential capability to represent robot language using the object-oriented concept.