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Break differential-drive limits for an omnidirectional robot

15 Feb 2013  | Eileen Su, Yeong Che Fai, Tey Wei Kang

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Hardware selection
We chose the NIsbRIO9632xt as the main controller for the omnidirectional mobile robot for several reasons. At 400MHz, with a 256MB non-volatile storage and 128MB DRAM for deterministic control and analysis, the controller offers high-speed computing for actuator speed and navigation control. The analogue input and output ports are sufficient for the omnidirectional robot. The availability of an Ethernet port makes it possible to connect the board to a laptop or a desktop computer via LAN. The user can access the board wirelessly from their own computers whenever there is available internet coverage.

An additional advantage to using this controller is that the NIsbRIO9632xt uses Labview as programming platform. Labview provides some functional blocks, such as PID controller and serial communication VIs. The VIs can be implemented easily and effectively as the graphical programming method of Labview makes it easy to design applications. In the debugging process, Labview provided a simple graphical user interface (GUI) that allowed easy access to the main controller. To read data from the controller, Labview provided indicators and a graphing function, both of which are very useful, especially for tuning PID parameters.

[Editor's note: It is also worth noting that the work was entered into a design competition by National Instruments.]

GUI of functional blocks

Figure 9: The GUI of the designed QEI blocks and PWM block output.


Results & discussion
In the main controller, NIsbRIO9632xt, there two major parts: an FPGA and a processor. In the FPGA, several peripherals are programmed, including QEI functional blocks and PWM functional blocks. Figure 9 shows the graphical user interface (GUI) of the designed QEI blocks and PWM block output. The QEI interface is the main display of distance sensor and the shaft encoder value, including the logic level on each particular pin and the accumulated pulses from each sensor. The PWM output shows the real time PWM output pulses on that particular PWM pin.

For navigation, the robot can travel to the desired coordinate with the desired orientation accurately. The resultant movement is shown in the photographs below.

Robot navigation

Figure 10: The omnidirectional mobile robot performs several navigation routines without changing its orientation. A: starting position at origin; B: navigates in Y-direction; C: navigates in X-direction; and D: navigates in X-Y direction.


Conclusion
An omnidirectional mobile robot and its navigation system have been successfully developed. This robot can navigate in any direction with or without changing its orientation, surpassing the conventional differential-drive method in propelling a mobile robot. The basic peripherals such as, QEI block and PWM block were developed to interface with the rotary encoder and motor driver, respectively.

The NIsbRIO9632xt has provided the processor speed need for in-time computation for actuator speed and direction for smooth motion. The navigation algorithm has been implemented easily using Labview. The whole system is integrated with wireless modem, allowing the robot to communicate wirelessly with the control unit. The robot has the capability to move autonomously but can also be controlled wirelessly if over-riding commands are needed. The potential applications for this robot are numerous, including Automated Guided Vehicle (AGV) for transport, commercial automobiles and surveillance systems.

- Eileen Su Lee Ming
  Senior Lecturer, Faculty of Electrical Engineering

- Yeong Che Fai
  Senior Lecturer, Centre for Artificial Intelligence and Robotics

- Tey Wei Kang
  Post-graduate student, Malaysia-Japan International Institute of Technology
  Universiti Teknologi Malaysia


This case study was submitted to the National Instruments' Graphical Systems Design Achievement Awards contest held last year. It is reproduced here after editing, with permission from National Instruments and the authors.


Eileen Su Lee Ming

Eileen Su Lee Ming received her B.E. and M.E. degrees in electrical and mechatronics engineering from Universiti Teknologi Malaysia, in 2001 and 2005, respectively, and the PhD degree in bioengineering from Imperial College London, United Kingdom, in 2010. She is currently a senior lecturer at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia. Her research focuses on medical electronics, rehabilitation robotics and virtual reality systems.


Yeong Che Fai

Yeong Che Fai received his B.E. (Mechatronics) and M.E. (Electrical) degrees from Universiti Teknologi Malaysia, in 2001 and 2005, respectively, and received the PhD degree in bioengineering from Imperial College London, United Kingdom, in 2010. He is presently a senior lecturer with the Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia. His research focuses on robotics for rehabilitation, mobile robotics, control and automation.


Tey Wei Kang

Tey Wei Kang received the B.E. (Mechatronics) degree from Universiti Teknologi Malaysia in 2012. He is currently a post-graduate under Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia. His research focuses on the tele-operation of mobile robots, wireless control and automation.

 

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