Autonomous Agricultural Robot

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ABSTRACT

This master thesis documents the work of group 1032b and concerns the development of a model based (Fault Detection and Iso-lation) FDI scheme to detect and isolate faults in an four wheeled agriculural robot called an API (Autonomous Platcare Instrumentation system). The thesis describes a number of different methods for deploying different FDI strategies as well as preliminary testing on a preexisting non-linear model of the robot.

Furthermore the thesis documents the efforts to make the API a more reliable and robust platform with the design at implementation of new sensors as well as steps to make is possible for the robot to diagnose itself. The different FDI methods were tested successfully on the non-linear model and were able to detect and isolate some of the selected faults. A new inclino meter was designed and implemented on the robot to replace the old. Two new proximity sensors were designed and implemented.

SYSTEM DESCRIPTION

The following chapter is an overview of the different components that the API is equipped with. The different components are placed on the outside of the robot as well as inside the two compartments placed in front and in the back of the robot.

External Components:

For communication the API is equipped with three different antennas:

  • A GPS antenna used for communication between the GPS receiver and the available GPS satellites.
  • An omni-directional radio modem antenna for communication between the GPS receiver and the GPS base station to facilitate the use of DGPS.
  • Combined WLAN device and antenna with USB interface for communication between the OBC and the API base station

Internal Components:

The front and rear compartments contain a number of common components:

 Components Attached to Outer Structure of the Robot

Components Attached to Outer Structure of the Robot.

Additional Hardware and Software:

When the API robot was handed over from the last group it was clear that some modifications were necessary on the physical system in order to make it more robust and more usable for the current and future projects. The main problems were:

  • The implemented WLAN solution was not very robust or very usable as it didn’t have the desired range and was prone to failure when used.The interface electronics boxes which are a very important link between the LH28s and the different sensors and actuators related to each where l were unreliable and impractical in use.
  • The only inclinometer on the API robot housed in the compass is fluid based and sensitive to vibrations to the point where the measurements are nearly unusable.
  • The API robot did not have any proximity sensors to detect obstacles in its path

Inclinometer:

The API is in the current state, assumed, to always be level. This is not a viable solution, when driving in fields, as the pitch and roll of the API affect both GPS and compass readings as well as introducing disturbances to the estimation of χM. The reason the robot is assumed level, is due to noise in the inclinometer built into the compass.

The Placement of the Two Proximity Sensors

The Placement of the Two Proximity Sensors.

MODELING

Model:

This chapter contains the kinematic and dynamic model of the API robot. The kinematic model is a mathematical model, which maps the orientation and angular velocity of the wheels to the movement of the robot.

API Geometry

As mentioned in the System Description, the API is a four wheeled robot with an steering actuator and propulsion actuator on each wheel. The dimensions of the API is shown in Fig. 4.1

Dimensions of the API Robot[

Dimensions of the API Robot.

Kinematic Model:

The purpose of the kinematic model is to map the steering angles of the wheels and the angular velocity of the propulsion actuators (βi and  ̇φi) to the velocities of the robot. The velocities are given by the vector  ̇χN.

Hybrid Modeling:

As the dynamic model described in the previous section is non linear, a linear model based approach to FDI is not directly applicable. To facilitate the use of linear model based FDI , a hybrid model of the non-linear system is developed. The concept is to make a complete hybrid approximation of a given path before the API robot begins to traverse the field.

FAULT DETECTION AND ISOLATION

Introduction:

The ability to detect, isolate and if possible accommodate faults or failures is especially important in an autonomous system because it is meant to run unsupervised for long periods of time. So to ensure safe operation and to maximize the time the system can continue operation an FDI -scheme must be designed and implemented in the API robot. This task has already been undertaken once by another group which laid the foundation for the continued work in the current project.

The Different Subsystems of the API Robot.

The Different Subsystems of the API Robot.

Fault Analysis:

The following contains a fault analysis of the steering and propulsion system on the API robot as well as the newly implemented inclinometer and proximity sensors. This includes the sensors.

Isolability analysis:

The purpose of the isolability analysis is to determine how different inputs to the system affect the state of the system. Specifically to identify inputs which affect the system in different ways and if this changes with the state of the system. This will identify if input faults can be isolated.

Linear Model Based FDI of Steering and Propulsion System:

This chapter deals with the design and implementation of a linear model based FDI scheme. As described in Sec. 4.4 on page 46 the behavior of the API robot is highly non-linear. This means that in order to take advantage of the many methods for linear FDI a hybrid approach is proposed in order to make a piece wise linear model. The hybrid scheme consists of a hybrid observer which observes the current hybrid state of the non-linear API system.

Nonlinear Particle Filter Based FDI of Steering and Propulsion System:

The chapter describes the design and implementation of a particle based approach for FDI. The focus of the project until now has been the linear approach, due to its relative maturity compared to a non-linear approach as the particle filter FDI method is. The non-linear approach is chosen as a Proof-of-Concept, due to computational limitations, which will be explained in detail later.

The Structure of the PF-FDI Method

The Structure of the PF-FDI Method.

Active Fault Isolation Supervisor:

As described in Cha. 7 on page 71, the chosen FDI scheme is only able to isolate faults down to which wheel pair is faulty as seen in Fig. 7.1 on page 72 , when driving straight whereas only fault detection is possible when turning. In order to provide complete fault isolation down to which sensor or actuator is responsible for the deviation of the API, a Active Fault Isolation scheme(AFI) is to be implemented. The purpose of the AFI is to take control of the API in case of a detected fault. The  AFI will then perform a series of tests in order to isolate the faults.

CONCLUSION

Acceptest of Linear FDI:

The two methods that make up the linear FDI scheme will be tested in this chapter and the results analysed. The two methods are integrated into a SIMULINK block as described and tested using the scenario described  As it was decided to only detect faults when the robot was driving straight.

A simple way of detecting this was implemented in the matlab code. A simple threshold on θ was used to detect weather the robot was turning or driving in a straight line. A second threshold was implemented on the Euclidean norm of the observer residue in order to detect when the BFDF isolation should be activated.

It proved to be a challenge to tune the different thresholds in the the BFDF to be able to detect different faults correctly.  It was however possible to get the FDI scheme to detect and isolate a few faults reliably. The the following page and No Actuation show the results of faults being introduced after 3 seconds o n wheel 1 and 3.

Source: Aalborg University
Authors : Martin Holm Pedersen | Jens Lund Jensen

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