ODOMETRY OF A CRAWLER MOBILE ROBOT · With the GPS sensor in the Ardupilot system, ... that shown...
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1. Introduction Navigation can be described as the process to determine a secure and suitable trajectory between a initial point and a goal point. This can be applied to a robot which is travelling between these points. Different sensors have been used for this purpose, therefore giving a large variety of solutions to perform navigation. The navigation of a mobile robot has three fundamental objectives: autonomous navigation, localization and mapping. This project presents the main characteristics of two different hardware-software architectures and the implementation of visual odometry and mapping for controlling an autonomous mobile robot called “Killer Crawler” in real environments. This vehicle is a rover- type research platform with dual-wheels motorized, which operates in rough terrain under arbitrary conditions. The whole system is designed to allow easy reconfiguration and adaption for new tasks. The hardware architecture, has a dedicated computer based in OS Ubuntu 12.10, where all the functions of the vehicle are controlled using cameras for data acquisition and hardware drivers to interface sensors and actuators. The objective of this project is present the development of a visual odometry and mapping method for an autonomous Crawler platform, that includes the design of hardware and software based in low level programming. Contributions In this work, a custom hardware and software system was developed using low level programming for the Crawler robotic platform. Visual navigation system based on stereo cameras with a sensorial system was used. The main contributions of this work were: A design of mechanical and electrical system that allows the Crawler platform control. An implementation of a navigation control system based in ArduRover project. An implementation of the odometry and mapping algorithm using visual navigation with a stereo camera system The development of an odometry system using an inertial sensor and a GPS sensor. The kinematic analysis of the Crawler robotic platform. M.I. Fernando Israel Ireta Muñoz, Dr. Fernando Ireta Moreno, Dr. Andrew I. Comport & Dr. Maxime Meilland * 2. Robotic platform The hardware and control software development for the Crawler mobile robot is based on the odometry, which is essential for the navigation. Different systems were implemented and tested individually to verify the correct performance of the robot. The main features of the Crawler robotic platform are: Dedicate onboard CPU Stereo cameras system Motor speed drivers Video Transmitter system Ardupilot APM 2.0 Battery level indicator Power voltage source Radio control system Pan – Tilt system 2.1 Kinematic analysis The kinematic analysis of the Crawler robotic platform is based in the Ackerman configuration for a pair of two motorized wheels with double symmetric steering (Figure 1). In this analysis is used the extended Ackerman condition. The resulting equations for the Crawler platform are shown in the Equations 1, 2 and 3. (1) (2) (3) 3. Results 3.1 GPS Navigation With the GPS sensor in the Ardupilot system, using the Nemea protocol and a DGPS strategy, it is possible to get the velocity and global position of the robot to display after the trajectory in a map server (example: Google maps). Figure 4. Trajectory given by the GPS. 3.2 Inertial navigation Integrating the IMU sensor and filtering the signals, as is shown in the flow code, the inertial odometry can be plotted in a graph, that shown the direction and the distance traveled. Figure 5. Odometry using the inertial sensor 3.3 Visual navigation C++ implemented software has been developed as an application called D6DVO. This software allows to check the mapping and navigation in real time using a calibrated stereo system. It was also implemented a direct video transmission system pluged in the Pan-tilt camera in the front, which send direct images to the ground station. Figure 6. D6DVO Interface Figure 1. Double steering Ackerman configuration. The turns radius for the robotic platform is given by the Equation 4. (4) where: 2.1.1 Kinematic model To consider the kinematic model, it is assumed that the robot is placed on a flat surface with orthonormal inertial basis as is shown in the Figure 2(a), where the linear and angular velocities are defined by and , respectively. Figure 2 Figure 2. a) Robotic platform in the inertial frame b) Free body diagram The relation between the angular wheel velocities and the velocities of the robot are: (5) Where η is the new control input introduced at the kinematic level, represents the intensity of the longitudinal velocity and is the chassis instantaneous velocity of rotation. Finally, the kinematic model for a car – like vehicle is the Equation 10. (6) where Φ is the steer angle of the wheel and L is the wheel base distance. 2.1.3 Kinematic control The trajectory contains n vectors, which are defined as: Figure 3. Regulated error to follow the trajectory. The translation error between the reference point and the current position is then defined by: (7) Where the rotation matrix is: and the angular error is directly defined by: Finally, the control law is: (8) ODOMETRY OF A CRAWLER MOBILE ROBOT u 1 u 2
Transcript of ODOMETRY OF A CRAWLER MOBILE ROBOT · With the GPS sensor in the Ardupilot system, ... that shown...
1. Introduction
Navigation can be described as the process to determine a secure and suitable trajectory between a initial point and a goal point. This can be applied to a robot which is travelling between these points. Different sensors have been used for this purpose, therefore giving a large variety of solutions to perform navigation. The navigation of a mobile robot has three fundamental objectives: autonomous navigation, localization and mapping.
This project presents the main characteristics of two different hardware-software architectures and the implementation of visual odometry and mapping for controlling an autonomous mobile robot called “Killer Crawler” in real environments. This vehicle is a rover-type research platform with dual-wheels motorized, which operates in rough terrain under arbitrary conditions. The whole system is designed to allow easy reconfiguration and adaption for new tasks. The hardware architecture, has a dedicated computer based in OS Ubuntu 12.10, where all the functions of the vehicle are controlled using cameras for data acquisition and hardware drivers to interface sensors and actuators. The objective of this project is present the development of a visual odometry and mapping method for an autonomous Crawler platform, that includes the design of hardware and software based in low level programming.
Contributions
In this work, a custom hardware and software system was developed using low level programming for the Crawler robotic platform. Visual navigation system based on stereo cameras with a sensorial system was used. The main contributions of this work were:
A design of mechanical and electrical system that allows the Crawler platform control.
An implementation of a navigation control system based in ArduRover project.
An implementation of the odometry and mapping algorithm using visual navigation with a stereo camera system
The development of an odometry system using an inertial sensor and a GPS sensor.
The kinematic analysis of the Crawler robotic platform.
M.I. Fernando Israel Ireta Muñoz, Dr. Fernando Ireta Moreno, Dr. Andrew I. Comport & Dr. Maxime Meilland *
2. Robotic platform
The hardware and control software development for the Crawler mobile robot is based on the odometry, which is essential for the navigation. Different systems were implemented and tested individually to verify the correct performance of the robot. The main features of the Crawler robotic platform are:
Dedicate onboard CPU
Stereo cameras system
Motor speed drivers
Video Transmitter system
Ardupilot APM 2.0
Battery level indicator
Power voltage source
Radio control system
Pan – Tilt system
2.1 Kinematic analysis
The kinematic analysis of the Crawler robotic platform is based in the Ackerman configuration for a pair of two motorized wheels with double symmetric steering (Figure 1). In this analysis is used the extended Ackerman condition. The resulting equations for the Crawler platform are shown in the Equations 1, 2 and 3.
(1)
(2) (3)
3. Results
3.1 GPS Navigation
With the GPS sensor in the Ardupilot system, using the Nemea protocol and a DGPS strategy, it is possible to get the velocity and global position of the robot to display after the trajectory in a map server (example: Google maps).
Figure 4. Trajectory given by the GPS.
3.2 Inertial navigation
Integrating the IMU sensor and filtering the signals, as is shown in the flow code, the inertial odometry can be plotted in a graph, that shown the direction and the distance traveled.
Figure 5. Odometry using the inertial sensor
3.3 Visual navigation
C++ implemented software has been developed as an application called D6DVO. This software allows to check the mapping and navigation in real time using a calibrated stereo system. It was also implemented a direct video transmission system pluged in the Pan-tilt camera in the front, which send direct images to the ground station.
Figure 6. D6DVO Interface
Figure 1. Double steering Ackerman configuration.
The turns radius for the robotic platform is given by the Equation 4. (4)where: 2.1.1 Kinematic modelTo consider the kinematic model, it is assumed that the robot is placed on a flat surface with orthonormal inertial basis as is shown in the Figure 2(a), where the linear and angular velocities are defined by and , respectively.
Figure 2Figure 2.. a) Robotic platform in the inertial frameb) Free body diagram
The relation between the angular wheel velocities and the velocities of the robot are:
(5)
Where η is the new control input introduced at the kinematic level, represents the intensity of the longitudinal velocity and is the chassis instantaneous velocity of rotation. Finally, the kinematic model for a car – like vehicle is the Equation 10.
(6)
where Φ is the steer angle of the wheel and L is the wheel base distance.
2.1.3 Kinematic controlThe trajectory contains n vectors, which are defined as:
Figure 3. Regulated error to follow the trajectory.
The translation error between the reference point and the current position is then defined by:
(7)
Where the rotation matrix is:
and the angular error is directly defined by:
Finally, the control law is:
(8)
ODOMETRY OF A CRAWLER MOBILE ROBOT
u1
u2
