Outdoor Mobile Robot Research Articles

옥외용 이동로봇의 분산점 칼만 필터 기반 3차원 위치 인식

This paper proposes a practical method, for evaluating 3-D positioning of outdoor mobile robots using the Unscented Kalman Filter (UKF). The UKF method does not require the linearization process unlike conventional EKF localization, so it can minimize effects of errors caused by linearization of non-linear models for position estimation. Also, this method does not require Jacobian calculations difficult to calculate in the actual implementation. The 3-D position of the robot is predicted using an encoder and tilt sensor, and the optimal position is estimated by fusing these predicted positions with the GPS and digital compass information. Experimental results revealed the proposed method is stable for localization of the 3D position regardless of initial error size, and observation period.

Multi-sensor fusion localization algorithm for outdoor mobile robot

In this paper, the multi-sensor fusion positioning technology of outdoor mobile robot is studied, and a real-time outdoor positioning algorithm combining GPS and lidar odometry is proposed. Lidar odometry based on 3D lidar for point cloud feature matching is realized. At the same time, a covariance matrix reflecting the local positioning information of the lidar odometry is constructed in real time according to the matching distance and the absolute median difference, and the accuracy factor, the circular probability error, etc. are used as the standard GPS. The positioning effect is evaluated to measure the respective positioning effects of the lidar odometry and GPS. On this basis, the GPS is used as the global factor in the pose structure, and the lidar odometry is used as the local factor to construct the pose observation constraint, and the global nonlinear trajectory optimization is carried out to realize the outdoor real-time positioning of the mobile robot. Experimental results and data analysis verify the effectiveness and practicability of the proposed method.

Development of Direction Sensor using Polarized Light in the Sky for Outdoor Mobile Robot

Development of Direction Sensor using Polarized Light in the Sky for Outdoor Mobile Robot

옥외용 이동로봇의 확장 칼만 필터 기반 3차원 위치평가 방법

This paper proposes a 3D localization method for an outdoor mobile robot. This method assesses the 3D position including the altitude information, which is impossible in the existing 2D localization method. In this method, the 3D position of the robot is predicted using an encoder and an inclination sensor. The predicted position is fused with the position information obtained from the DGPS and the digital compass using extended kalman filter to evaluate the 3D position of the robot. The experimental results showed that the proposed method can effectively evaluate the 3D position of the robot in a sloping environment. Moreover, this method was found to be more effective than the conventional 2D localization method even in the evaluation of the plane position where altitude information is unnecessary.

Developing a Portable Human–Robot Interaction (HRI) Framework for Outdoor Robots Through Selective Compartmentalization

One of the challenges of outdoor robots is developing effective portable Human-Robot-Interaction (HRI) frameworks. Hand-held devices offer a practical solution. By equipping these devices with robot software, they can be made to interact with the outdoor robots. Android devices are ideal as they are open source and can be integrated with robots powered by the Robot Operating System (ROS), also open source. However, due to the limits of rosjava, the mechanism that links ROS with android, and the conflicting modes of operation between ROS and android, current implementations of ROS-android offer limited robot applications that do not support advanced operations such as autonomous navigation and others. This paper implements selective compartmentalization to overcome these limitations, by combining ROS with android through a number of ROS and android bridges that would facilitate the development of advanced robot applications. Through the proposed method, authors were able to develop a portable HRI framework that allowed human operators to supervise an outdoor mobile robot while it performed an autonomous task. From their mobile devices, users were able to initialize the robot, configure its motion, and monitor its progress. Also, users were able to reprogram the robot to perform new tasks (not previously planned) through a creative use of features offered in the developed HRI framework. Also, user cognitive effort was reported to be low as evident by the positive score on the NASA-TLX scale test which was corroborated with robot performance data. This paper presents the detailed development and implementation steps.

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization.

이동로봇의 분산점 칼만 필터 기반 옥외용 위치평가

This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.

3-D Soil Compaction Mapping Through Kriging-Based Exploration With a Mobile Robot

This letter presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs, and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructed offline using kriging, a form of Gaussian process regression. This process is laborious and costly, limiting the quality and resolution of the resulting information. Instead, we propose to use an outdoor mobile robot for automatic collection of soil condition data, building soil maps online and also adapting the robot's exploration strategy on-the-fly based on the current quality of the map. We show how using kriging variance as a reward function for robotic exploration allows for both more efficient data collection and better soil models. This letter presents the theoretical foundations for our proposal and an experimental comparison of exploration strategies using soil compaction data from a field generated with a mobile robot.

Outdoor Mobile Robotic Assistant Micro-Controller Module (Arduino), Firmware and Infrared Sensor Circuit Design and Implementation, Operating Principle and Usage of Pirmotion Sensor

This paper presents an outdoor mobile robot that can be controlled remotely using an android phone. With the help of the sensors, the robot was programmed to navigate within the bounded field, detect the motion in the surroundings and can also send an alarm. The robot was designed to be able to navigate to the flowerpots placed in the zone and dispense water to those pots. The robot has proved capable of watering a total of 600 ml to two different plants in a predefined area. The robot, if operating in a manual mode, can be used to remotely water plants in a radius of 9 meters. On top of this, it emits no carbon and produces no RoHS by-products and can thus be deemed 100% eco-friendly.

Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR.

Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments.

Compensated Heading Angles for Outdoor Mobile Robots in Magnetically Disturbed Environment

Heading information is critically important for autonomous mobile robots as it is necessary for scanning or sweeping predetermined areas for specific tasks. Fusing sensor data including angular rates, acceleration, and geomagnetic fields provide heading and attitude. However, the geomagnetic field is often interfered with by ferromagnetic objects or other magnetic sources, resulting in incorrect heading information. This paper describes an algorithm that detects and rejects magnetic disturbances contained in a geomagnetic field. This algorithm combined with an extended Kalman filter is implemented in a relatively low-cost, small-scale microprocessor and sensor module. The algorithm is detailed for parameters that detect magnetic disturbances. The algorithm is also evaluated outdoors by driving a mobile robot on a lawn with apparent ferromagnetic objects and on the flat roof of a ferroconcrete building that includes iron bars and electrical wires in or under the roof. The experimental results on a flat roof indicate that the algorithm improves the accuracy of the heading significantly by reducing the peak-to-peak error by 32.9% (or the rms error by 69.9%).

A new explicit dynamic path tracking controller using generalized predictive control

Outdoor mobile robots must perform operations with ever-increasing speed and distance. Therefore we are interested in designing controllers of fast rovers which improve mobile robot capacity in natural environment. When designing autonomous path tracking systems for fast rovers, a major problem is the dynamic effect and the non-linearity of the model. Several control laws have been designed to resolve the problem by separating the dynamic of the robot at the problem of trajectory tracking.

Auto-Calibration Methods of Kinematic Parameters and Magnetometer Offset for the Localization of a Tracked Mobile Robot

This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on a tracked electrical mobile robot. The mobile robot is aimed to be adopted as a tool to help humanitarian demining operations.

Study on Driving a Bumped Slope of Mobile Robot According to Changing the Damping Coefficient of Suspensions

Most of outdoor mobile robots have a suspension on each wheel in order to relieve the shock by ground obstacles and to improve the driving stability. Typically, in the actual operations, the suspensions have been used under a given set of conditions as all the damping and spring coefficients of the suspensions are fixed. However, it is necessary to readjust the coefficients of the suspensions according to surface conditions that may cause the unstable shaking of a robot body at high speed driving. Therefore, this paper is focused on the mobility analysis of an outdoor robot when the coefficients of suspensions (in particular, damping coefficients) are changed while driving on an uneven road surface. In this paper, a semi-active suspension with twelve damping coefficient levels was used and a small sized vehicle with the suspensions was employed to analyze the mobility dependent on a change of the damping coefficient. And the mobility was evaluated through driving experiments on a bumped slope.

Navigation of mobile robot using Low-cost GPS

A method of fusing data from a Global Positioning System (GPS) and a Dead Reckoning (DR) system for outdoor navigation of a Wheeled Mobile Robot (WMR) is proposed. The low-cost GPS receiver cannot be utilized by itself for navigating the mobile robot. Since, it produces an error of approximately 10–20 meters. DR can provide precise navigation data to the mobile robot but its errors accumulate over time. Since, all the previous data are utilized to calculate the current position information. DR needs to be complemented by other navigation sensors to navigate the mobile robot. The proposed GPS/DR data fusion method is based on the characteristics of the single GPS receiver output. The fused data provides accurate and robust navigation information to the outdoor mobile robot. Simulations were conducted using real GPS data which were then compared with the results using a Kalman filter that verified the potential of the proposed GPS/DR data fusion method.

Development of Battery Charging System Using Wireless Power Transmission for Outdoor Mobile Robots

Development of Battery Charging System Using Wireless Power Transmission for Outdoor Mobile Robots

2A1-U02 カメラ画像内の路面エッジ検出による屋外自律ロボットの自己位置・姿勢推定の検証

2A1-U02 カメラ画像内の路面エッジ検出による屋外自律ロボットの自己位置・姿勢推定の検証

1P1-J07 Development of an Outdoor Mobile robot for High-Speed Human-Following Navigation

1P1-J07 Development of an Outdoor Mobile robot for High-Speed Human-Following Navigation

Research of Autonomous Navigation Strategy for an Outdoor Mobile Robot

A kind of autonomous navigation strategy for an outdoor mobile robot is proposed in the real world environment. Encoder disk, electronic compass and GPS are used in the dead reckoning system of outdoor mobile robot. To reduce dead reckoning error, the extend Kalman filter method is adopted to realize data fusion from encoder disk and electronic compass. GPS receiver is used to correct the dead reckoning data to eliminate the accumulation error of the dead reckoning. Laser range finder is used to detect the obstacle and establish the environment map. Filed D* algorithm and DWA method are used to realize the obstacle avoidance and path planning. The experimental results show that the outdoor mobile robot can realize the autonomous navigation under the complex road condition.

An GPS/DR navigation system using neural network for mobile robot

Dead reckoning (DR) is frequently used for mobile robot navigation as it can provide precise short term navigation information but the errors of a DR system can accumulate over time. A global positioning system (GPS) can be used for outside navigation and localization but the error of a single GPS receiver is still big even though an error intentionally introduced into the system called the selective availability policy (SA) was already removed. Standard differential GPS (DGPS) can provide an accuracy of less than one meter but it is too expensive for the mass market aside from the need of having a base station to provide differential data. This paper proposes a new GPS/DR fusion method based on the data characteristics of a cheap single GPS receiver and use neural network to estimate the output of the GPS receiver to provide precise navigation information to the mobile robot. Simulation results validated the performance of the proposed method and showed its potential use in outdoor mobile robot navigation.