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        Yonghoon Ji (池勇勳) Ph.D.
Assistant Professor
Department of Precision Mechanics at Chuo University, JAPAN
[Curriculum Vitae] [Research] [Publication]
Recent research themes will be updated...

Military UGV [ January, 2010 ~ Present ]

Platform and Sensor Configuration
Pioneer 3AT (all terrain)
• The most popular outdoor robot
• Length: 0.65m, Height: 0.2m, Width: 0.66m
• Max speed : 0.7m/s, slope mobility : 25°, max payload : 30kg

Fig. 1 Platform and sensor configuration
Research Contents
DSM (digital surface model)
• Most popular maps to represent outdoor environments generated using an aerial mapping system
• Digital representation of ground surface using 2D grids
• Each grid has a single elevation information (2.5D)
• There are many discrepancies (DSM vs. real environment)

Fig. 2 Example of DSM built by aerial mapping system

Local 3D Map
• Accurate representation of the real outdoor environments built by a robot with tilting laser scanner
• Each grid contains the number of surface level and the minimum, maximum elevation at each level
• ICP (iterative closest points)-based integration of local maps (outdoor SLAM)

Fig. 3 ICP-based outdoor 3D SLAM

Combination of DSM and Satellite Image for Virtual Reality
• Texture mapping on DSM using satellite image
• We can confirm the understanding of environment become much easier than before combination of satellite image

Fig. 4 Combination of DSM and satellite image for virtual reality

Particle filter-based outdoor localization
• Localization by matching the environment model and sensor data
• Reference map is built by aerial mapping system or robot with tilting laser scanner
• Monte Carlo localization (MCL): based on range sensor for map matching

Fig. 5 Concept of map matching-based outdoor localization

Mov. 1 Particle filter-based local localization based on DSM

Accurate update of DSM by using local 3D map
• To overcome the limitation of DSM representation
• 2.5D DSM and local 3D map can be represented at once

Fig. 6 Effect of updating DSM: non-updated DSM built by aerial mapping system, and updated DSM fused with local elevation map

Mov. 2 Accurate update of DSM by using local 3D map

Related Paper
• Yong-Ju Lee, Yong-Hoon Ji, Jae-Bok Song, and Sang-Hyun Joo, “Performance Improvement of ICP- based Outdoor SLAM Using Terrain Classification,” Proceeding of the International Conference on Advanced Mechatronics (ICAM 2010), pp. 243-246, October, 2010, Osaka, Japan.

Yong-Hoon Ji, Sung-Ho Hong, Jae-Bok Song, and Ji-Hoon Choi, “DSM Update for Robust Outdoor Localization Using ICP-based Scan Matching with COAG Features of Laser Range Data,” Proceeding of the IEEE/SICE International Symposium on System Integration (SII 2011), pp. 1245-1250, December, 2011, Kyoto, Japan.

Surveillance Robot [ January, 2011 ~ December, 2011 ]

Platform and Sensor Configuration
• Length: 0.65m, Height: 0.2m, Width: 0.66m
• 2 tracks for driving and steering, 2 flipper arms (stair climbing is available)
• Max speed : 1.5m/s, slope mobility : 45°, max payload : 15kg

Fig. 1 Platform and sensor configuration
Research Contents
GPS-based outdoor localization
• Extended Kalman filter (EKF)-based sensor fusion
• Odometry and roll, pitch yaw from IMU : used for prediction process of EKF
• GPS : used for update process of EKF

Fig. 2 EKF-based outdoor localization by using wheel odometry and GPS information

Gradient method-based outdoor global path planning
• Optimal path generation using map information from initial position of robot to goal
• Extended 2D gradient method
• Local minimum problem can be avoided
• Traversability map is used

Fig. 3 Global path extraction by gradient method

Implementation of a manipulator on tracked robot
• Mobile tracked robot + 4DOF manipulator based on stabilization control
• Efficient unmanned surveillance
• Absorbing vibration at rugged terrain while driving

Mov. 1 Manipulator with stabilization control

Autonomous navigation
• Environment : indoor and outdoor
• Localization, path planning and motion control algorithms are integrated

Mov. 2 Autonomous navigation of tracked robot

Related Patent
• Jae-Bok Song, Yong-Hoon Ji, Jae-Kwan Ryu, Jong-Won Kim, and Joo-Hyun Baek, “ Apparatus for estimating location of moving object for autonomous driving,” Korean Intellectual Property Office (KIPO), #10-2012-0025468.

• Jae-Bok Song, Yong-Hoon Ji, Jae-Kwan Ryu, Jong-Won Kim, and Joo-Hyun Baek, “Method for estimating location of mobile robot,” Korean Intellectual Property Office (KIPO), #10-2012-0025469.

Transportation Robot [ July, 2010 ~ April, 2012 ]

• Length: 0.8m, Height: 1.0m, Width: 0.5m
• 2 motors for steering, another 2 for propulsion
• Top speed : 1.0m/sec (flat ground, no rider condition)

Fig. 1 Transportation robot platform

Research Contents
Lane extraction
• Image processing, segmentation, clustering and labeling methods are used
• Lane extracted stably by picking up features generated by using segmentation and clustering
• Extracted lane markers are useful to local localization in outdoor

Fig. 2 Image processing to extract lane feature

Obstacle avoidance
• DWA (dynamic windows approach)
• Simultaneous obstacle avoiding algorithm
• Dynamic window : Area in velocity space to reach without collision to obstacle during the given time step with given robot velocity
• Determining the velocity in the dynamic window to reach the goal point fast

Fig. 3 Determining DWA velocity from objective function

Mov. 1 DWA-based obstacle avoidance

Related Paper
Yong-Hoon Ji, Ji-Hun Bae, Jae-Bok Song, Joo-Hyun Baek, and Jae-Kwan Ryu, “Outdoor Localization through GPS Data and Matching of Lane Markers for a Mobile Robot,” Journal of Institute of Control, Robotics and Systems, Vol. 18, No. 6, pp. 594-600, June, 2012.