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Project card[edit]

  • Project Name: Study and implementation of a self-localization system for mobile robot navigation on rough terrain.
  • Author: Juan Camilo Gómez Cadavid (
  • Academic Year: 2011-2012
  • Degree: Master
  • Jde Version: jde-4.3
  • SVN Repository: [1]
  • Tags: Rough terrain, AUV, IMU, Inertial localization
  • Technology: C, C++, JdeRobot, GTK+, dsPIC-Microcontroller based custom hardware, IMU-MEMs sensors
  • State: Finished

The mobile robot platform[edit]

Mobile robot description[edit]

The RTT mobile robot[edit]

The mobile robot RTT (view Fig 1) is a rover-type research prototype Rocker-Bogie suspension [1][2], six-wheel independent, grouped in two sets of three wheels on each side of the vehicle and secured by means of an articulated structure. The rear wheels are linked to the robot's body by a rigid arm known as Rocker, and the same is fixed by means of a pivot, a second arm known as the bogie, which holds the middle and front wheels. A differential mechanism that connects the two rocker arm to the body of the vehicle, keeps the RTT balanced even when the trains are at different heights. Overall, the differential system further the articulated structure, seek to ensure that all six wheels are always kept in contact with the floor, allowing a permanent traction.

Rtt pic.jpg

Figure 1: Rover-type research prototype mobile robot (RTT)


The mobile robots in specific applications as in agriculture, surveillance, search and functions on industrial environments must face to the difficult conditions of navigation that present the terrain where they operate. Although suspension mechanisms allow the operation of mobile robots in rough terrain, the problem is that the navigation systems start to generate unwanted behaviors because their localization techniques are affected by changes in the rugged terrain and constant noise in the sensorial measurements. The objective of this project is to study and implement an auto-localization system for mobile robot navigation in rough terrain. The system to implement aims to tackle the difficult navigation conditions that occur on uneven terrain where they can make few assumptions when it is desirable to plan the navigation of the robot. We expect that the system will provide the robot the ability to self-locate within their environment and the ability to navigate along the terrain answering to the spatial alterations that should be arise.

Project Objectives[edit]

  • Propose a local self-localization system for the navigation of the mobile robot navigation RTT on rough terrain, taking into account the constraints of its mechanical structure.

For this main objetive, is necessary:

  • Analyze different techniques of local self-localization applicable to robot RTT.
  • Define the hardware and software architecture to accomplish the tasks of self-localization on rough terrain.
  • Design and implement necessary hardware and software to estimate the robot location in the environment, taking into account the structural constraints of the robot RTT.
  • Validate the operation of self-localization system by implementing autonomous navigation tasks on rough terrain.


Hardware and Software architecture[edit]

  • Sensorial System

Proprioceptive Sensors: The RTT has sensors to inform several aspects of the state in which the robot is, these are: -Current sensors (indirect torque measure)

-Orientation sensors

-Battery level sensors

-Encoders - Wheels Angular speed

-Attitude Sensors

Exteroceptive sensor: Supported by a SRF02 ultrasonic sensor array [3], located taking into account the reflective characteristics typical of an ultrasound wave and considering the maximum detection focus that includes the angular width and the minimum and maximum distance detected. Similarly, it is considered the effects of crosstalk noise referred to the crossing of signals between ultrasonic sensors. The configuration used in the RTT is composed of 7 ultrasonic sensors located as shown in Fig 2. Each sensor uses an I2C communication system. The distance measurements are managed through a USB interface, by a dedicated computer with a data acquisition system custom designed, using a 16-bit embedded microcontroller.

Srf02 dist rtt.jpg

Figure 2: Distribution of ultrasound sensors. (Circular panorama and detection focus).

Inertial sensor: Three CruizCore® R1001E fully self-contained MEMS digital gyroscopes for measuring heading angles were used. Each uses a digital UART-USB board as communication interface with the dedicated computer, and are located in each axes of the robot’s body frame. The R1001E has 50Hz bandwidth and precisely measures angular rates up to ± 100 °/sec, it can also measure rates up to ± 150 °/sec with lesser accuracy. There is also a low cost Freescale® MMA7260® 3-axis accelerometer as second source of redundant information for tilt estimation and an electronic compass for alternative estimate of the robot pose. This sensors, allow estimate the robot attitude. The attitude is usually expressed in terms of three special angles known as “Euler angles”. The angles are φ, θ, and ψ, which are usually referred to as roll (sometimes also called “bank angle”), pitch (also called “elevation”), and yaw (also called “heading” or “azimuth”) respectively. Rates of rotation of the body frame relative to the navigation frame can be expressed in terms of the derivatives of the Euler angles, [4][5]. The inertial measurement unit (IMU) is shown in Fig 3.

Imu rtt.jpg

Figure 3: RTT Inertial measurement unit (IMU)

  • Hardware Control System

The control unit has an onboard-compact dedicated computer, with an Intel ® Atom ™ N270 1.60 GHz, with sufficient processing speed to perform autonomous navigation algorithms and control systems. Sensor data and control signals are handled by the computer through USB, using two data acquisition interfaces (DAQ).

The primary DAQ interface board (DAQ-1) is formed by a digital signal controller (DSC) dsPIC33FJ256MC710 [6], which is responsible for sending control signals to each of the traction motors through low-level PID controllers, providing stability and ability to function in a variety of terrain. In addition has the function of collecting and processing the signals from the proprioceptive sensors.

The second DAQ interface board (DAQ-2) uses a PIC18F4455 microcontroller [6], which aims to receive information from a set of exteroceptive sensors that provide data and inertial environment and state of the robot respectively.

Communication interfaces with the central processing system are established via the USB port, while other peripherals for sensor managing are performed using different communication buses such as I2C, SPI and RS232.

Additionally, it profits from computer's wireless card for making a call via WiFi, for monitoring, teleoperation and platform programming. The WiFi allows communication range up to 200 meters and the possibility of extending this distance using routers in repeater configuration [7].

H arch rtt.jpg

Figure 4: RTT Hardware Architecture

Improving localization performance on rough terrain[edit]

Cross Coupled Control (CCC) (Spanish Version-Pending for translation into English)[edit]

Los vehículos terrestres con más de tres motores cinemáticamente independientes, son definidos como vehículos sobreactuados (over-constrained ó cinemáticamente restringidos). Generalmente esta definición se extiende para todo sistema mecánico que posee más actuadores que grados de libertad [1]. Muchos robots móviles son sobreactuados, por ejemplo, los rovers de exploración marciana desarrollados por la JPL de la NASA, Rocky, Sojourner, Spirit y Oportunity [2]. Para este tipo de vehículos, sus ventajas respecto en las estructura mecánica, también genera problemas en el sistema odométrico, los cuales que deben ser considerados para un posicionamiento más preciso. Aqui se presenta el desarrollo de un control por acople cruzado (CCC), para disminuir las fuentes de errores odométricos en robots móviles sobreactuados, como el Slipping y Skidding, además de ciertas mejoras en el lazo de control.


Cuando un robot se desplaza a bajas velocidades y sobre una superficie de baja tracción como la arena, es inevitable que se presente Skidding y Slipping excesivo en las ruedas [4][5]. El Skidding (Derrape), se refiere a la perdida de tracción cuando el vehículo en movimiento, tiene una rueda bloqueada por fricción excesiva en el tren de accionamiento, ó por frenado del motor, por tal motivo, los encoders en las ruedas con skidding, producen menos pulsos que las ruedas con tracción; a diferencia del Slipping (deslizamiento), que es perdida de tracción cuando se provee demasiada potencia a los motores, provocando sobre-aceleración, de este modo las ruedas con Slipping generan más pulsos que las ruedas con tracción. Estos fenómenos son producidos las siguientes razones [4]:

  • En controladores de velocidad convencionales, el software de control prescribe velocidades de referencia separadas (Set-Point independientes), a las ruedas izquierda y derecha. Bajo condiciones normales, se presentan pequeños tiempos transitorios mientras los controladores alcanzan su punto de referencia. Sin embargo, la perturbaciones externas, (por ejm: deformidades en el terreno y el deslizamiento) interfieren con las velocidades de comando, causando tiempos transitorios adicionales, durante los cuales, las ruedas no siguen las velocidades de referencia en forma precisa. Este situación particular se agrava, cuando el robot se desplaza a velocidades muy bajas, ya que la fricción interna causada por engranajes y rodamientos del motor genera perturbaciones más significativas, hasta el punto en que las ruedas pueden llegar a detenerse o saturarse completamente.
  • En robots móviles con accionamiento diferencial, desviaciones momentáneas de la velocidad de referencia, solo reduce la precisión en la que el robot sigue una trayectoria determinada. Esto no es un problema relevante en la mayoría de aplicaciones. Sin embargo, en robots sobreactuados como el Pionner AT[6], los rovers de Marte [2], o el robot RTT [7][8], el problema es más severo. En dicha situación, se llegan a tener 4 o más motores controlados, pero con solo uno o dos grados de libertad independientes. Como resultado, todas las ruedas deben conservar la misma velocidad para evitar deslizamientos (Slipping). Pero incluso, cuando la orden es moverse en línea recta, cualquier desajuste momentáneo de las velocidades en las ruedas, hará que estas presenten Skidding o Slipping impredeciblemente.

Para reducir el fenómeno del deslizamiento y derrape (slip y skid), Borestein propone uso del "Cross Coupled Control” (CCC) ó “Control por acoplamiento Cruzado” [4].

The CCC Control[edit]

El CCC (Ver figura 5), compara constantemente las velocidades de las ruedas izquierda y derecha del robot y emite comandos correctivos a los motores para frenar el motor más rápido y acelerar el motor más lento. El efecto producido por esta técnica, es una coincidencia más precisa de las velocidades en las ruedas izquierda y derecha del robot, incluso en presencia de perturbaciones internas y externas. En [4] y [5] se demuestra que esta técnica también ayuda a corregir errores odométricos, mejorando el sistema de auto-localización.

Wheel Skidding/Slipping detection[edit]

Inertial Odometry and Sensor Fusion[edit]


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