Difference between revisions of "2nd Level Course Projects"

In order to speed up the development and the maintenance of embedded applications, a way to update the firmware on a microcontroller without the need of connecting cables or programmers can be very handy. We are developing a framework for rapid prototyping of low-cost robots, with smart devices that exchange data on a CAN bus network. The CAN bus bootloader is one of the components we need for this project, enabling remote firmware upgrades of all the devices connected to the CAN network.

This project aims to develop a CAN bus bootloader for STM32 ARM Cortex-M3 microcontrollers, and eventually for other architectures.

No special skills are required, except basic c and object oriented programming.

The aim of the project is to design, implement, test, and evaluate a new version of the device. While the charge phase, implemented by an ad-hoc IC controlled by the PIC, is quite efficient, some work has to be done in order to design a new solenoid and improve the way the PIC implements a sort of modulation of strength of the shoot, in order to implement small passages between robots. The final purpose is to improve the efficiency of the system, with the aim of minimize the energy consumed and maximize the energy transmitted to the ball, in order to obtain more powerful shots.

The first part of the project is focused on some theoretical aspects in order to understand the current design and evaluate how to improve it, while the second phase will be focused on the implementation and test of a new prototype of the kicking device. Experience with PIC-based systems is a plus, but not required, while some experience with electronics circuits is highly recommended. Students are supposed to work in the lab following a set of safety guidelines and rules with circuits at 400V, with 10A pick current during charge phase, and even more during shots. Student from electronics engineering are really welcomed to choose this project.

1. http://www.er.ams.eng.osaka-u.ac.jp/robocup-mid/index.cgi

When developing embedded applications it is frequently needed to look at *hardware register content* in order to *debug the code*. All commercial development suites offer register views that show their contents as well as the meaning of each bit. Open source development solutions currently lack this feature, meaning that you have to look to the correct memory location and map the content to the corresponding register bits manually. This seems to be one of the most limiting issues when developing embedded application using open source solutions.

This project aims to fill this gap, developing an Eclipse plug-in that shows the register contents in a tree viewer, like most commercial suites do.

The aim of the project is first to improve the current design of the PIC-based board, and realize a new working prototype, and then to implement and evaluate different algorithms able to estimate more precisely the x,y and theta odometric data from the mice readings. Experience with PIC-based systems and some experience with electronics circuits is a plus. Students are supposed to redesign the electronic board, improve the firmware of the PIC, and work on the algorithm that estimates the robot position on the PC. It would be also interesting to evaluate the possibility to embed the optimization and estimation algorithms in the firmware of the PIC in order to produce a stand-alone device.

Ask the tutors of the project for extra material, such as data-sheets and other documentation.

• implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
• developing a, easy to use, procedure for the calibration of IMU parameters
• making a comparison with commercial units using a robot arm as testbed
• validate the accuracy of the IMU on a flying platform
• integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)

Material

• electronic board and eclipse based C development toolkit for ARM processors
• papers describing the algorithms we are interested in implementing

Expected outcome:

• few different AHRS algorithms with comparative results
• user-friendly procedure to calibrate the IMU
• a sistem which integrated IMU and GPS to provide accurate positioning

Required skills or skills to be acquired:

• C programming on ARM microcontroller
• background on kalman filtering and attitude estimation

When developing embedded applications it is common the need to test some algorithm in some fast way, without to re-program the whole firmware every time. PAWN (http://www.compuphase.com/pawn/) is a *simple and lightweight scripting language with a C-like syntax*. Execution speed, stability, simplicity and a small footprint were essential design criteria for both the language and the abstract machine, making PAWN suitable for embedded applications.

This project aims to port the abstract machine to ARM Cortex-M3 microcontrollers, add a set of functions to interface with the underlying hardware peripherals and then to embed it as ChibiOS/RT (http://www.chibios.org) thread.

• Implementation of mechanical and electronical parts of the robots for the management of the ball and kicking
• Design of robot behaviors (fuzzy systems)
• Coordination of robots
• New sensors

These projects allow to experiment with real mobile robots. Participation to the championships is a unique experience (2000 people, with 800 robots playing all sort of games...)

The project can be turned into a thesis by facing different problems in depth.

TiltOne is a robot with only two wheels that can stand in vertical position following an unstable equilibrium point. The control is applied by commanding an amount of torque to the wheels, allowing the robot to mantain it's gravity center vertical aligned to the wheel axis.

The aim of the project proposal is to implement and compare different control solutions, based on classical approach (as PID and LQR control) and Machine Learning approach (as Reinforcement Learning control policies), that allow the robot to move following a given trajectory at a given speed.