Outdoor Autonomous Mobile Robot Research Articles

Navit(oo)n: Open Source Mobile Robot Project for Nakanoshima Robot Challenge

Recently, the legislation regarding autonomous mobile robots for outdoor pedestrian areas have been advancing, leading to increased expectations for task automation such as transportation and cleaning. Outdoor environments like parks, where vehicles cannot enter, present many three-dimensional terrains such as stairs and inclined surfaces, causing difficulty in achieving accurate environment recognition and autonomous movement. Furthermore, robots that navigate pedestrian walkways must be smaller and lighter than cars and also have a robust system capable of traversing steps and uneven surfaces and withstanding rainy weather. Currently, robots designed for paved roads are commercially available; however, robots capable of navigating park walkways are still in the research and development stage. Therefore, to accelerate the research and development of outdoor autonomous mobile robots, this study proposes the Navit(oo)n platform, designed for use in outdoor environments. This robot can be manufactured using easily obtainable parts, and all CAD data, circuit design data, and autonomous movement software are provided as open source. This paper introduces an overview of Navit(oo)n that successfully completed the course and achieved all tasks in the recent Nakanoshima Robot Challenge.

A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.

Usage of Autonomous Mobile Robots Outdoors - an Axiomatic Design Approach

Industry 4.0, growing material supply chains, and changing logistics structures require flexible material flow solutions. As a result of this development, the usage of autonomous mobile robots (AMRs) is increasing significantly. Although there has been much research undertaken on the design of indoor AMRs, there is a lack of research regarding outdoor systems. Weather and road conditions are still challenging for sensors and actuators. Furthermore, requirements regarding the system, which must be known and met beforehand to guarantee industrial applicability, are yet to be sufficiently determined. This paper aims to close this gap and identify functional requirements through Axiomatic Design, which is used to develop design guidelines for practitioners. Starting with a systematic literature review and semi-structured interviews, the authors gather basic customer requirements. These customer requirements will then be analyzed to define functional requirements. Through a mapping and top-down decomposition process, the research team deduces design solutions for using outdoor AMRs. These requirements and solutions will be transformed into guidelines, which help system designers to improve the implementation of AMRs on the factory premises.

2A2-C06 屋外移動ロボットのための経路情報設定に関する研究 : 人間が既存の地図を読み取って行う設定(車輪型/クローラ型移動ロボット(2))

2A2-C06 屋外移動ロボットのための経路情報設定に関する研究 : 人間が既存の地図を読み取って行う設定(車輪型/クローラ型移動ロボット(2))

2A2-R08 屋外自律移動ロボットへの移動経路設定の研究 : 既存の地図情報を人間が読み取ったナビゲーション(車輪型/クローラ型移動ロボット(2))

We research the method of route setting by environment information from the internet map read by human for outdoor autonomous mobile robot. In many cases, to make a robot perform autonomous movement, the data of the course is needed to be collected at fieldworks in advance. Thereby, the range which a robot can move autonomously was limited by collected data at fieldworks. Therefore, in the future, we will can set from the internet map information the route for the robot without prior fieldwork. In this paper, we suggest research strategies.

1A1-G07 屋外自律移動ロボットへの地図情報を利用した移動経路設定の提案 : 人間が読み取ったインターネット地図情報を用いたナビゲーション(車輪型/クローラ型移動ロボット(1))

In this paper, we propose the method of route and sensor's priority setting by environment information(e.g. many buildings, many trees, a clear or unobstructed space) from the internet map read by human for outdoor autonomous mobile robot. In many cases, making autonomous movement perform to a robot, in advance the data of the course needed to be collected at fieldworks. Thereby, the range which a robot can move autonomously was limited by collected data at fieldworks. Therefore, in the future, by acquiring environment information from the internet map information, we can set the route for the robot without prior fieldwork.

2P1-J09 カメラ画像内の特徴点から構成する四角形の重心を利用した屋外自律移動ロボットの姿勢制御(移動ロボットのための視覚)

2P1-J09 カメラ画像内の特徴点から構成する四角形の重心を利用した屋外自律移動ロボットの姿勢制御(移動ロボットのための視覚)

Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots

GPS and laser range finders are generally utilized in current robot navigation. However, information from the magnetic field and electronic compass is not, since it is dynamically changing at every position. In this paper, the relationship between the intensity of a magnetic field in the environment and its position is taken into account by utilizing a three-axis magnetic sensor to scan the magnetic field in the environment to build a database. The mobile robot navigates by performing trajectory tracking based on the database. The experimental results show that by applying the proposed method, the mobile robot is able to navigate in an outdoor environment with reliable accuracy.

2P1-D23 屋外環境下で用いる自律移動システムに関する研究 : 第46報屋外自律移動監視システムでの環境地図の使用による遠隔操縦機能の付加

2P1-D23 屋外環境下で用いる自律移動システムに関する研究 : 第46報屋外自律移動監視システムでの環境地図の使用による遠隔操縦機能の付加

MR센서를 이용한 실외형 자율이동 로봇의 퍼지 조향제어기에 관한 연구

본 논문은 MR(magneto-resistive)센서를 이용한 실외형 자율 이동 로봇의 퍼지 조향 제어기에 대한 연구이다. 2개의 MR 센서를 이용한 지구자기장의 제거방법을 제안하고, 각각의 센서에서 측정된 자기장 차이값(dBy, dBz)을 입력변수로 사용한 퍼지 조향 제어기를 설계하였다. 자율 이동 로봇 시뮬레이션 프로그램을 개발하기 위해 MR센서의 모델링, 이동로봇의 동적모델링, 좌표변환 등을 하였다. 컴퓨터 시뮬레이션은 퍼지 로직을 이용한 이동로봇 제어기의 주행성능을 검증하기 위해 사용되었고 그 시뮬레이션을 통하여 제안한 퍼지 조향제어기의 강인성을 검증하였다. This paper describes a fuzzy steering controller for an outdoor autonomous mobile robot using MR(magneto-resistive) sensor. Using the magnetic field difference values(dBy, dBz) obtained from the MR sensor, we designed fuzzy logic controller for driving the robot on the road center and proposed a method to eliminate the Earth magnetic field. To develop an autonomous mobile robot simulation program, we have done modeling MR sensor, mobile robot and coordinate transformation. A computer simulation of the robot including mobile robot dynamics and steering was used to verify the driving performance of the mobile robot controller using the fuzzy logic. So, we confirmed the robustness of the proposed fuzzy controller by computer simulation.

Development of the Neural Network Steering Controller for Outdoor Autonomous Mobile Robot Using Magnetic Fields

This paper describes a lateral guidance system for an outdoor autonomous mobile robot, using a neural network to model the magnetic fields from road embedded magnets. The analytical magnetic fields model was compared with measured data and found to have less than 1 % difference. The neural network was also used to learn the steering behaviour of the mobile robot relative to the magnetic field values (Bx, By, Bz). A computer simulation of the mobile robot (including robot dynamics and steering) was used to verify the steering performance of the robot controller using the neural network. Good results were obtained. Also, the real outdoor autonomous mobile robot using neural network controller verified good results.