Research & Development
Modular System (MES & MMS)
This project reports the first results of a previously developed MES - Modular Electronic System and MMS - Modular Mechanical System for autonomous mobile robots and for developing robots to use the MES and MMS in other scientific applications. This approach offers the ability to build a scalable and flexible control system with only a few modules. A novel feature of our approach is its modular integration of electronics with the ability to multitask, which can be adapted for particular applications. We demonstrate the robustness of this approach in controlling an indoor mobile robot for the EUROBOT 2009 and 2010.
Modular Mechanical System - MMS
Modular Electronic System - MES
Tracked Vehicle
The locomotion of mobile robots in the undeveloped outskirt area is one of the most difficult demands on the system. On one hand, as an outdoor robot it has to be fast and flexible on the other hand the vehicle has to deal with rough underground such as stones, gravel or stairs. Other important requirements are that the whole system is, on one hand robust, and on the other hand a lightweight construction to reduce the energy consumption and increase the agility. According to these requirements a drive system is developed which is shown in the figures.
One active flipper consists of two brushless motors; one motor drives the main pulley wheel, the second one is supporting the cantilever. The drive system basically consists of four pulley belts which are driven separately. Additionally the two belts (left and right side) and the middle belts can rotate individually. This is important for tasks like driving over uneven underground and climbing stairs. The body of the vehicle basically consists of an aluminum frame and the gaps, which are for reducing weight, are covered with carbon composite sheets.
SLAM - Simultaneous Localization and Mapping
The two dimensional map generation is based on the acquired data of a laser range finder (LRF) UBG-04LX-F01 from the company Hokuyo. The light source of the sensor is an infrared laser with a wavelength of 785nm (laser class 1). The sensor is connected to the USB2.0 port of the Mini ITX and communicates with the map building software which is programmed in C++. For generating the map the scan-matching algorithm ICP-Iterative Closest Point and Coreslam combined with landmarks is used. The laser range finder obtains data of a scanning angle of 240 and a maximum distance of 4m. It is mounted on the front side of the robot and is automatically levelled out by a mechanism in case that the robot tilts.
In order to detect position and attitude of the robot an inertial measurement unit, so called IMU, is used. The IMU consists of three accelerometers, to determine the acceleration in the three axes and three rate sensors, to detect the attitude. Additionally three magnet sensors are used to achieve the direction of the earth magnetic field for referencing the sensor.
Vision
For victim detection a thermo camera and one standard camera are mounted on the top of a robotic arm and provide pictures for the operator. A graphical user interface (GUI) is about to be developed which is supposed to display current information of the terrain and environment. Furthermore the GUI supposes the sensor data of the CO2 sensor, laser range finder and several other sensors.
Additionally the operator gets important information about the robot's battery status and warnings for the obstacle avoidance.
LPS - Local Positioning System
The basic navigation is based on infrared navigation system. The robot will measure the distances from the robot to the beacons. Three beacons are arranged on the sidelines of the field.
To locate the position of the robot infrared cameras are placed on the top of the robot. These cameras detect infrared signals (wavelengths between 870-950nm) which are sent by the beacons. The cameras in this system have a resolution of 1024 x 768 pixels. Furthermore the cameras can detect four different infrared LEDs in same time. When an infrared LED (Blob) is detected the cameras are able to calculate the X and Y coordinates of the Blob. This coordinates match Image coordinates of the camera. To get the exact position of the robot an intercept point calculation is needed. For this task at least two beacons have to be analyzed. The advantage of this system is that you don't have to know any x and y coordinates at the beginning of the run. Ambiguities of this measurement system after the start could be shut out within using the previous position. All the calculations will be done by an ATMEGA microcontroller.
