One of the most advanced navigation technologies in recent years is called SLAM. It is a technology that maps the environment through a robot's sensing system while exploring an unknown environment. This allows the robot to position itself and to reach its final destination through the created map. In this paper, we introduce a base technology to apply to SLAM using our new magnetic field sensing system. A magnetic field sensing systems were applied instead of visual sensors or distance sensors for the localization of robots in an indoor environment instead of a visual and distance sensors in this paper. Using surrounding magnetic field system can estimate the current global position of the robot and the direction of nearby magnetic objects. Finally, we show correct positioning of the robot through experiments using odometrical and magnetic field information in a real world learned indoor environment.
There has been a considerable amount of development in navigation in recent
years. Among the technologies that have emerged in this area, SLAM is one
of the key technologies for autonomous driving. SLAM can identify the current
location and position relative to the overall environment to estimate the
destination [
To do this, we applied the magnetic field sensing system to the positioning technology in an experimental environment and updated the robot’s position using magnetic field information. We performed real-life experiments by attaching a magnetic field sensing system made by our team to a mobile robot. The experiment was conducted in a compact space inside a laboratory. We measured the sensor values in the sensing system and conducted mapping based on magnetic objects. We used a total of 72 points at 10 cm intervals and checked the limitations and the sensitivity of the sensing system. Through this study, we were able to verify the possibility for laying the foundation of magnetic field based navigation technology.
The magnetic field sensing system we introduced in this paper consists of eight
analog sensors and a 12-V DC solenoid by placing eight of these analog magnetic
field sensors in a circular 45 degree interval, as shown in Fig. 1. From this
magnetic field sensing system, we can obtain two kinds of signals. We define these
signals as DC and AC signals as shown in Fig. 2. The DC signal is defined as
the change in voltage that can be obtained when the solenoid is switched on.
The AC signal refers to the amplitude of the voltage change when the solenoid
is switched on. Details of the configuration of this sensing system are described
in [
Normal state Metal Sphere 2 The experiment was carried out in a indoor laboratory of 350cm x 400cm size in Fig. 3. Three computer desktops and monitors were placed on the outside, and two metal trash cans were placed in the center. The surrounding magnetic field of experimental environment was measured by the magnetic field sensing system of the robot at each point as it moved at intervals of 10 cm through a total of 72 points. Using the data obtained from these experimental environments, a Magnetic Experience Map (to be introduced in the following sections) was constructed. Also, as shown in (d) of the Fig. 3, seven steps were performed and the current location was updated through our matching method with the magnetic experience map.
NSRVH'W Q0LWUR ' WUHQL3 QXW&JL LDHK0QF ŵϭϬĐ dƌĂŚƐŶ ŽƉƚĞƐŬ ƌDŽŶŝƚ (d) ƌDŽŶŝƚ dƌĂŚƐŶ ŽƉƚĞƐŬ There are two main ways to form the magnetic experience map : using the largest of eight sensor values and then expressing all eight sensors in Fig. 5.
The first method shows how a magnetic field is topologically formed for the Byungmun Kang, DaeEun Kim and Jiseung Jung ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ࡿ ૠ experimental environment. The second method allows us to visually estimate the direction of the magnetic objects. The map is created according to the two types of signals mentioned earlier, DC and AC. Through this map, we implemented robot behavior using existing memory or experience. 2.4
As mentioned in the system configuration section earlier, this magnetic field sensing system uses eight sensors for sensing the surrounding magnetic objects as shown in (b) of Fig. 4. Since the sensing system is circular, it can be used to detect all directions as the robot moves. The magnetic field sensing information can be used to determine or estimate the direction of the surrounding magnetic object. This method was motivated by sand scorpions and was implemented through the following formula.
m k=1 zeiφ =
zkeiφk = x + yi x = m k=1
m zk × cos(φk), y =
zk × sin(φk)
k=1
φ = arctan(y/x)
(1)
(2)
(3)
φ is the direction of the vibration signals, and the score for each leg of the sand
scorpion is zk(m=8), and the degree of each leg is φk, which can be expressed
as above equations [
Through the magnetic field sensing system, we can estimate the global magnetic direction of the robot. In other magnetic field sensing systems, when detecting surrounding magnetic objects through one magnetic field sensor, the magnetic object cannot be utilized as a compass due to disturbance. In contrast, since the system has multiple sensors, sensors that do not receive disturbance from magnetic objects can recognize signals from the magnetic field of the earth. As shown in Fig. 6, signals are obtained by rotating each sensor clockwise with regard to the east direction. The signals from the eight sensors can be obtained with specific x,y values according to their direction, and can therefore detect the global direction of the robot.
S0
S1
S2
S3
In this paper, the robot can detect the direction of magnetic objects around it
using information obtained in the experimental environment. We can obtain the
magnetic direction by using the formula (1),(2),(3) in the method section. This
can be applied to avoidance by recognizing magnetic obstacle as the robot moves
in the actual indoor environment, and also to determining the exact location of
its route. As shown the Fig. 7, the direction is given for magnetic fields with
strengths larger than a certain size. This allows for the representation of DC
signals and AC signals, respectively. Where DC signals can be determined at
various points in the macro, AC signals can be determined at a location close to
their own objects, but there is some noise. In fact, in the [
Without Mag sensor With a Mag sensors With 4 Mag sensors With 8 Mag sensors
Pos 1 Pos 2 Pos 3 Pos 4 Pos 5
(b)
matching method was used to update the current position using magnetic field
information while giving a command to the robot. In fact, in [
The results were compared with the results of moving the robot without magnetic field information on a designated path based on user’s command and using magnetic field information. Also, the results were divided into three types. The first type is to update the path of the robot using magnetic information from one magnetic field sensor placed in the head direction of the robot. As shown in cyan color line of Fig. 8, based on the surrounding magnetic field information obtained from one sensor, the results are updated with an odometrical error. Since one sensor is used to find the magnetic direction we performed a rotation action. In the 5 update positions, the average error is about 48.71 pixels (29.22 cm) which is similar to a method that does not use a magnetic field. Basically, one pixel is corresponding to 0.6 cm for distance. The second type is to use only four sensor information out of a total of eight sensors in the front, rear, and side directions (e.g., north east and west). This type was less rotated than the first type, and the robot can find the magnetic direction in the magnetic experience map and update it, as shown in the green dashed line of Fig. 8. The average error of the second type is 39.75 pixels(23.85cm). The last type is to use all eight sensor information that can be obtained from magnetic field sensing systems. Unlike the previous first and second types, the robot can update its position without rotation action as shown in the blue dashed line of Fig. 8. The average error is also 24.63 pixels (14.77 cm). The average error value for 5 iterations of robot test is 47.45 pixels (28.47 cm) when the magnetic field information is not used as shown in the red dashed line of Fig. 8, and if the surrounding magnetic field information of the magnetic sensing system is used, it can be seen that the robot can reach the specified position based on user’s command in (b) of Fig. 8. 4
In this paper, by using the magnetic field sensing system developed by us, a magnetic experience map of the experimental environment could be obtained, and the global direction could be determined through the array of magnetic field sensors. In addition, the robot was positioned in an indoor laboratory environment and the direction of the magnetic object around it was used to position its current position. Basically, sensing systems and methods for determining the orientation of magnetic objects were constructed based on biomimicry. In the robot experiment based on the command of actual users, the results were better than the experiments obtained by using only the robot’s odometrical information, and we obtained more accurate results by using 8 sensor data of 2 types through the array of 8 sensors and the solenoid at the center. This proved the sensing system suitable for the positioning technology based on indoor navigation.
This work was supported by the Institute of BioMed-IT, Energy-IT, and SmartIT Technology (BEST), a Brain Korea 21 plus program, Yonsei University.