- Research Article
- Open Access
Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks
© Akai et al.; licensee Springer. 2014
- Received: 14 January 2014
- Accepted: 20 June 2014
- Published: 21 November 2014
In order for a robot to autonomously run in outdoor environments, a robust and stable navigation method is necessary. Especially, to run in real-world environments, robustness against moving objects is important since many pedestrians and bicycles come and go. Magnetic field, which is not influenced by the moving objects, is considered to be an effective information for autonomous navigation.
Localization technique using a magnetic map, which records ambient magnetic field, has been proposed. The magnetic map is expressed as a linear map. When using this linear magnetic map, swerving from the desired path is a fatal problem. It is because that the magnetic map contains only magnetic data on a desired path. In the paper, we propose a novel navigation method which allows a robot to precisely navigate on a desired path even if localization is performed on the basis of the linear magnetic map. The navigation is performed by using a control method based on a DCS (Distributed Control System). In the system, several navigation modules are executed in parallel, and they independently control the robot by using magnetic and geometric landmarks.
Results and discussion
We conducted three navigation experiments. Our robot could perfectly accomplish all navigation even if it was disturbed by many moving objects during the navigation.
The control method based on the DCS could switch the navigation module for controlling the robot to cope against the change of its surroundings. The precise and robust navigation was achieved with the proposed method.
- Magnetic navigation
- Distributed control system
- Mobile robots for public space
- Autonomous navigation
An autonomous mobile robot can be used as a service robot in various fields such as transportation, cleaning, and guiding. To realize these services, a robust and stable navigation method is necessary.
Autonomous navigation methods using artificial landmarks (e.g. magnetic tapes or makers) have been proposed . In some factories, automated guided vehicles traveling on magnetic tapes are practically used. However, using such a system requires environmental arrangement.
In contrast, autonomous navigation methods, which do not depend on artificial landmarks, have been recently proposed. For indoor navigation methods, the accuracy have been reached a practical level (e.g. ). However, for outdoor navigation methods, dynamic changes in the environment still remains as a major problem. It is, therefore, necessary to strengthen the robustness of the outdoor navigation methods against changes in the surroundings.
We have proposed an outdoor navigation method called “MN (Magnetic Navigation) ,”. MN has robustness against moving objects since localization and control are performed by using only ambient magnetic field. We achieved the long distance run over 1 km as the mission of Tsukuba Challenge 2009 . However, many trial runs and a lot of time ware required.
To solve this problem, we have proposed to use ambient geometric landmarks for compensating the robot’s position (MNPC: Magnetic Navigation with Position Compensation ). The MNPC does not require many trial runs to achieve long distance navigation. However, in areas where magnetic anomalies occur, trajectory of the robot meanders. If magnetic anomalies occur in a wide range, the robot swerved from the desired path. It is a fatal problem since the MNPC uses a linear magnetic map, that contains only magnetic data on the desired path.
In this paper, we aim to improve the MNPC. For the improvement, we focused on a DCS (Distributed Control System). In the MNPC, there is one navigation module that controls the robot using magnetic sensor and LIDAR (Light Detection and Ranging) readings. If either magnetic sensor readings or LIDAR readings contains incorrect information, the control does not work well. However, precise navigation can be performed if sensor readings, which do not contain incorrect information, can be selected. Therefore, we divide the navigation module into several modules, and propose to use every module based on the DCS.
In this paper, we explain the proposed method and conduct two navigation experiments. Moreover, we took part in a guiding demonstration, which was held in Tsukuba-city, Japan, during annual conference of ROBOMEC2013. By these results, the effectiveness and robustness of the proposed method are shown.
Distributed control system
Thus far, many types of DCS have been proposed. Brooks have proposed SA (Subsumption Architecture) . Recently, SA is used as typical DCS. In SA, layers of control system are built to let a robot operate at increasing levels of competence. Rosenblatt have proposed DAMN (Distributed Architecture for Mobile Navigation) . In DAMN, an arbiter performs command fusion and selects an action which best satisfies the prioritized goals of the system by voting. Morales et al. have proposed SSM (Sensor Sharing Manager) . In SSM, many processes run in parallel such as localization, obstacle detection, and navigation. These DCSs allow the robot to generate robust and/or flexible behaviors.
In the proposed DCS, we focus on achieving a precise navigation. In our DCS, there are several modules and they generate the same behavior (trajectory tracking). Moreover, they respectively have a priority value, and this value is determined based on concordance rate of sensor readings and the database. The navigation module with the highest priority value is selected to control the robot. As the result, our robot can precisely navigate on the desired path.
Autonomous navigation method
Outdoor navigation methods using LIDARs or cameras have been proposed (e.g. ,). Especially, localization technique based on ICP (Iterative Closest Point) algorithm  or MCL (Monte Carlo Localization)  is widely used since it allows a robot to precisely localize. However, since these sensors observe geometric landmarks, the localization accuracy can be easily affected by dynamic changes in its surroundings. In contrast, our proposed method allows the robot to stably navigate in dynamic outdoor environments even if LIDAR readings are used. It is because the DCS selects to use the MN instead of the navigation module using LIDAR readings.
As we mentioned in previous section, navigation methods using artificial magnetic landmarks have been proposed . Bento et al. have proposed a navigation method using magnetic makers in semi-structure outdoor environment . However, expensive initial cost is necessary in order to apply these method in wide outdoor environments. Moreover, these arrangements may spoil landscape of the city.
Localization technique using magnetic field, that occurs in natural environment, have been proposed. Suksakulchai et al. and Haverinen et al. have proposed localization technique using magnetic anomalies in indoor environment ,. In these methods, linear maps are used for recording magnetic field and only the magnetic field on the desired paths were stored on the maps. However, these literatures do not present control technique for the robots. Since the maps do not have magnetic data other than on the path, swerving from the desired paths is a fatal problem.
Recently, localization technique using a 2D magnetic map has been proposed . Frassl et al. achieved precise localization based on the 2D magnetic map . However, constructing the 2D magnetic map in wide environments is difficult, namely adopting this technique to outdoor navigation is also difficult. Our method can achieve precise autonomous run even if localization is performed on the basis of the linear magnetic map.
An estimation method of the robot’s heading direction using ambient magnetic field has been proposed . However, as far as we know, none of these kinds of methods can achieve a long distance run. We then consider that this estimating method should be combined with another method to achieve a long distance run. In our method, localization technique using magnetic field and the control based on the DCS are combined.
In our method, since localization is performed by using the linear magnetic map, localization accuracy is not influenced by moving objects. However, since the map does not have enough information, it is difficult for the robot to precisely navigate on the desired path. Our method allows the robot to precisely navigate on the path by using DCS.
Magnetic navigation with position compensation
An example of the database used on the MNPC
The MNPC controls the robot as converging all components of e c to zero by controlling robot’s angular velocity ω.
We achieved outdoor autonomous navigation over 2 km by using the MNPC . However, the MNPC has a fatal problem.
A fatal problem of the MNPC
As we mentioned in the previous section, swerving from the desired path is a fatal problem when using the linear magnetic map. However, the trajectory easily meanders in an area with magnetic anomalies since the control using magnetic sensor readings does not work well. As the result, the robot easily swerves from the path. In MNPC, geometric information is used for compensating an error produced by the meander. However, if ambient geometric information is also dynamically changed, the compensation does not work well. This means that it is difficult to adopt the MNPC in dynamic environment navigation.
Avoiding moving objects, which intersect the desired path, is also fatal problem since the robot swerves from its desired path. However, if the robot can precisely navigate on its desired path, avoiding moving objects is not necessary. Therefore, in our navigation strategy, the robot will stop when an obstacle appears in front of it.
The key idea of our proposed method is to use only effective sensor readings for robot’s control. The MNPC has three different information of magnetic sensor, LIDAR, and encoders readings. The quality of these information differs from each others. For instance, there is a case where LIDAR readings are not containing incorrect information even if magnetic sensor readings are containing incorrect information. In this case, meandering the trajectory can be prevented if LIDAR readings can be only used for the control. Therefore, we divide the navigation module of the MNPC into several modules, and propose to use every module based on the DCS.
There are several navigation modules in the system and each module called a “navigator”. Each navigator is defined to navigate the robot based on each sensor readings. Each navigator independently send a control input u i (i=1,2,…,k) and a priority p i to the management module. This management module then selects an actual control input u based on the priority value, which is determined by comparing external sensor readings (m t and g t ) with the database (m n and g n ). The g t is geometric information observed at time t. If sensor readings are unreliable, the priority value of the navigator using those sensor readings is set to a low value. With this method, an reliable navigator can be selected.
Navigator based on odometry
The navigator converges this deflection to zero by controlling the angular velocity.
Navigator based on geometric information
The navigator converges all components of e l to zero by controlling the angular velocity.
Navigator based on magnetic information
The navigator converges this deflection to zero by controlling the angular velocity.
Navigator based on magnetic and geometric information
This navigator uses magnetic and geometric information, namely it is the MNPC. The detail of the MNPC is mentioned above.
where α i are plus fixed numbers. We set a high fixed number to a navigator, which can precisely navigate the robot. The order of the priority vales is as following; (1) the MNPC (2) navigator based on magnetic information (3) navigator based on geometric information (4) navigator based on odometry. It was defined on the basis of our experiences.
In a case where moving objects across nearby the robot, the navigator using geometric information does not work well. The reliability of LIDAR readings is determined by using these difference values |gl,n − gl,t| and |gr,t − gr,n|. If the values exceed 1.0 m, the priority of the MNPC and the navigator based on geometric information become zero.
where Δ b max is a difference value of maximum and minimum magnetic intensity b z measured by used magnetic sensor. In this study, we defined Δ b max = 1 on the basis of our experiences. Magnetic sensor readings are reliable when the value of p m closes to 1. If p m is less than 0.998, the priority value of the the MNPC and the navigator based on magnetic information become zero.
We conducted a navigation experiment for verifying the effectiveness of the proposed method by comparing five navigation methods; the proposed method and four navigators in the proposed method. Note that the experiment was conducted after three days of database construction.
Trajectory tracking errors from the desired path
Standard deviation [m]
Simulated guiding demonstration
After the groups walked away, the robot used geometric information for the lateral error compensation and a stable navigation was restored. The experimental result shows that the proposed method can be adopted to navigation in a busy pedestrian walkway.
Guiding demonstration in ROBOMEC2013
The guiding demonstration using autonomous mobile robots was held in Tsukuba-city, Japan, and we took part in this demonstration. In this demonstration, robots should lead persons from the start area to the destination in actual busy pedestrian walkway.
Figure 14(d) shows an interesting case where there was a truck nearby the desired path. The truck influenced magnetic field and blocked detection of available geometric landmarks. In this case, the navigator based on odometry was performed. The robot ran approximately 10 m and achieved autonomous navigation. Autonomous navigation based on odometry is usually unsuitable, however, in this case, it is suitable since there are no effective information. Our method could select navigator based on odometry and realized stable navigation in the dynamic environment.
In one demonstration, the robot ran approximately 700 m. We conducted 27 guiding demonstrations and our robot could perfectly accomplished all demonstrations. From these results, the robustness of the proposed method was confirmed.
In this paper, we proposed a navigation method for improving the MNPC. The improvement could be fulfilled by using the DCS. In our method, since the linear magnetic map is used for localization, swerving from a desired path is fatal problem. Our proposed method could solve the problem and achieved precise autonomous navigation in dynamic outdoor environments, since used DCS can select the navigator, which precisely navigates the robot on the desired path. We took part in the guiding demonstration held in the central of Tsukuba-city, Japan. In the demonstration, we conducted 27 guiding, and miss guiding was not done. From these results, we showed the effectiveness and robustness of the proposed method.
This study was supported by KAKENHI (25420210). Moreover, the first author was supported by Mr. Saito.
- Lee SY, Yang HW: Navigation of automated guided vehicles using magnet spot guidance method. Robot Comput-Integr Manuf 2012,28(3):425–436. 10.1016/j.rcim.2011.11.005MathSciNetView ArticleGoogle Scholar
- Maaref H, Barret C: Sensor-based navigation of a mobile robot in an indoor environment. Robot Autonomous Syst 2002, 38: 1–18. 10.1016/S0921-8890(01)00165-8MATHView ArticleGoogle Scholar
- Rahok SA, Ozaki K: Localization system based on magnetic map for indoor mobile robot. In JSME conference on robotics and mechatronics. Nagano, Japan,; 2008. 5–7 June 2008 (in Japanese)Google Scholar
- Rahok SA, Shikanai Y, Ozaki K: Navigation using an environmental magnetic field for outdoor autonomous mobile robots. Adv Robot 2011,25(13–14):1751–1771. 10.1163/016918611X584677View ArticleGoogle Scholar
- Nagatani K, Kushleyev A, Daniel DL: Sensor information processing in robot competitions and real world robotic challenge. Adv Robot 2012,26(14):1539–1554. 10.1080/01691864.2012.694624View ArticleGoogle Scholar
- Akai N, Rahok SA, Inoue K, Ozaki K: Implementation of a long-distance navigation method of low cost structure that combines a localization using magnetic information and a lateral position compensation. Trans Japan Soc Mech Eng 2013,79(799):681–690. (in Japanese) 10.1299/kikaic.79.681View ArticleGoogle Scholar
- Brooks RA: A robust layered control system for a mobile robot. IEEE J Robot Automation 1986,2(1):14–23. 10.1109/JRA.1986.1087032View ArticleGoogle Scholar
- Rosenblatt JK: DAMN: a distributed architecture for mobile navigation. J Exp Theor Artif Intell 1997,9(2–3):339–360. 10.1080/095281397147167View ArticleGoogle Scholar
- Morales Y, Carballo A, Takeuchi E, Aburadani A, Tsubouchi T: Autonomous robot navigation in outdoor cluttered pedestrian walkways. J Field Robot 2009,26(8):609–635. 10.1002/rob.20301MATHView ArticleGoogle Scholar
- Yoshida T, Irie K, Koyanagi E, Tomono M (2010) A sensor platform for outdoor navigation using gyro-assisted odometry and roundly-swinging 3D laser scanner In: International conference on intelligent robots and systems, Taipei, Taiwan,18–22 October 2010.View ArticleGoogle Scholar
- Irie K, Yoshida T, Tomono M: Outdoor localization using stereo vision under various illumination conditions. Adv Robot 2012,26(3–4):327–348. 10.1163/156855311X614608View ArticleGoogle Scholar
- Chen Y, Medioni G (1991) Object modeling by registration of multiple range images In: International conference on robotics and automation, California, United States,9–12 April 1991.View ArticleGoogle Scholar
- Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte Carlo localization for mobile robots In: International conference on robotics and automation, Michigan, United States,10–15 May 1999.View ArticleGoogle Scholar
- Bento LC, Nunes U, Moita F, Surrecio A: Sensor fusion for precise autonomous vehicle navigation in outdoor semi-structured environments. In International conference on intelligent transportation systems. Congress Center MesseWienNeuVienna, Austria,; 2005. 13–16 September 2005Google Scholar
- Suksakulchai S, Thongchai S, Wilkes DM, Kawamura K (2000) Mobile robot localization using an electronic compass for corridor environment In: International conference on systems, man and cybernetics, Nashville, United States,8–11 October 2000.View ArticleGoogle Scholar
- Haverinen J, Kemppainen A: Global indoor self-localization based on the ambient magnetic field. Robot Autonomous Syst 2009, 57: 1028–1035. 10.1016/j.robot.2009.07.018View ArticleGoogle Scholar
- Gozick B, Subbu KP, Dantu R, Maeshiro T: Magnetic maps for indoor navigation. IEEE Trans Instrum Meas 2011,60(12):3883–3891. 10.1109/TIM.2011.2147690View ArticleGoogle Scholar
- Frassl M, Angermann M, Lichtenstern M, Robertson P, Julian BJ, Doniec M (2013) Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion In: International conference on intelligent robots and systems, Tokyo, Japan,3–8 November 2013.View ArticleGoogle Scholar
- Woong K, Roh KS, Sung HK (2006) Particle filter-based heading estimation using magnetic compasses for mobile robot navigation In: International conference on robotics and automation, Florida, United States,15–19 May 2006.View ArticleGoogle Scholar
- Akai N, Inoue K, Ozaki K: Autonomous navigation based on magnetic and geometric landmarks on environmental structure in real world. J Robot Mechatronics 2014,26(2):158–165.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.