Kazunori Ohno

Collaborative mapping of an earthquake-damaged building via ground and aerial robots

We report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake-damaged building. The goal of the experimental exercise is the generation of three-dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics.

Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion

The authors aim at map based outdoor navigation of a mobile robot. In navigation, robot position is fundamentally obtained by odometry. However, the position is misaligned as the robot moves because odometry has cumulative error. DGPS measurement data may cancel its position error. The framework of EKF is used for the modification and the fusion between odometry and DGPS measurement data. The DGPS measurement data, however, could have large error because of multipath near buildings. In this paper, the authors propose a method which eliminates erroneous DGPS measurement data when odometry robot position is fused, and confirm the validity of this approach.

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.

Semi-autonomous control system of rescue crawler robot having flippers for getting Over unknown-Steps

A rescue crawler robot with flipper arms has high ability to get over rough terrain, but it is hard to control its flipper arms in remote control. The authors aim at development of a semi-autonomous control system for the solution. In this paper, the authors propose a sensor reflexive method that controls these flippers autonomously for getting over unknown steps. Our proposed method is effective in unknown and changeable environment. The authors applied the proposed method to Aladdin, and examined validity of these control rules in unknown environment.

Real-Time Robot Trajectory Estimation and 3D Map Construction using 3D Camera

Our research objective is simultaneous localization and mapping (SLAM) in rubble environment. The map construction requires estimation of robot trajectory in 3D space. However, it is hard to estimate it by using odometry or gyro in rubble. In this paper, the authors proposed real-time SLAM based on 3D scan match. 3D camera is used for measurement of 3D shape and its texture in real-time. 3D map and robot trajectory are estimated by combining these 3D scan data. ICP algorithm is used for the matching method. The authors modified ICP algorithm as fast and robust one for real-time 3D map construction

Development of 3D laser scanner for measuring uniform and dense 3D shapes of static objects in dynamic environment.

The authors aim at the development of a 3D laser scanner that can measure uniform and dense 3D shape of static objects in dynamic environment. The 3D scanner was composed of a 2D Laser Range Finder (LRF) and Pan-Tilt base. 3D shape is measured by rotating 2D LRF that is tilted around the two axes. The laser point trajectory shows cross scan. Use of cross scan achieved the wide view angular and the uniform 3D scan. The proposed 3D scanner can adjust the measurement area and the density of 3D point clouds by changing the angle and angular velocity of the pan-tilt mechanism. The 3D scanner can decrease 3D measurement time. In addition, this 3D scanner measures same area twice during one 3D scan. Comparing these two measurement distances, the moving object can be detected.

Dense 3D map building based on LRF data and color image fusion

Research objective of the authors is 3D map building and localization of search robot for rescue use. In this paper, the authors propose a novel method of dense 3D map building and present its trial result. For building a map, it is necessary to estimate robot motion. However, on rubble, it is difficult to estimate robot motion by using odometry or gyro. Therefore, in this framework, rough 3D map and discrete robot motions are derived using SLAM based on 3D scan matching. ICP algorithm is used for the matching method. Then, the dense 3D map is reconstructed from the rough 3D map and texture images.

Outdoor map building based on odometry and RTK-GPS positioning fusion

The final objective of the authors is to realize mobile robot navigation in walkway of outdoor environment. The path along the walkway which is measured correctly together with landmarks must be given in the framework in this paper. The robot identifies its position by means of odometry, DGPS and laser range finder (LRF) during the motion along the path. This work contributes to the map building of landmarks along the walkway. To make the map, RTK-GPS, odometry and LRF are used for the sensory devices and Kalman smoothing technique is also incorporated to interpolate trajectory of the scanning LRF equipment between the two point that RTK-GPS measurement took place. The scanning by LRF is performed to acquire the 3-D shape of objects along the walkway. The trajectory presumed properly and well organized shapes of the objects are reconstructed.

Robotic control vehicle for measuring radiation in Fukushima Daiichi Nuclear Power Plant

The authors developed a robotic control vehicle for measuring the radiation in the Fukushima Daiichi Nuclear Power Plant. There are a lot of hotspots in the nuclear power plant (over several tens mSv/h). Heavy radiation prevents humans from searching and reconstructing it. It is essential to measure the radiation to ensure worker safety. The developed robotic control vehicle can measure radiation using a γ-cam and TALON with a radiation sensor. A heavily shielded operation box was built for reducing the exposure of radiation to 1/3. Two operators control the TALON and the γ-cam from the shielded operation box. Because of its pinhole mechanism, the γ-cam needs to know the distance to the targets. The 3-D light detection and ranging (LIDAR) was built for distance measurement. It has wide measurement range of up to 20m. Using the 3-D LIDAR and the shielded operation box can reduce the exposure during radiation measurement. The use of the developed robotic control vehicle can realize safe radiation measurement.

Integration of a sub-crawlers' autonomous control in Quince highly mobile rescue robot

Rapid information gathering during the initial stage of investigation is an important process in case of disasters. However this task could be very risky for human rescue crews, when the infrastructure of the building has been compromised or the environment contaminated by nuclear, biological, or chemical weapons. To be able to develop robots that can go inside the site instead of humans, several area of robotics need to be addressed and integrated inside a common robotic platform. In this paper, we described the modular interoperable and extensive hardware and software architecture of Quince, a high degree of mobility crawler type rescue robot having four independent sub-crawlers. To facilitate Quinces navigability, we developed and integrated a semi-autonomous control algorithm that helps the remote operator driving Quince while the flippers are autonomously adjusting to the environment. The robot is then able to overcome obstacles and steps without a special training of the operator. We present here the software integration and the control strategy of the flippers using the embedded basic version of Quince.