-Specify, design and implement a typical embedded system。

-Apply embedded software design techniques

The other 50% of the module was related to microcontrollers hardware and programming in low level languages。

Students were instructed that the slave vehicle needed to be capable of maintaining a set distance whilst following the master vehicle’s speed profile (pseudo-random)。 In order to determine the safe distance they needed to take into consideration the distance between the leader and the follower。 The design had to be implemented and verified using LabVIEW and two National Instruments Robotic kits (‘DaNI robotic vehicle’)。 Although they were not forced to use fuzzy logic in their implementation, it was given as a hint for proposed control strategy and some students attempted it while others opted for a simple control strategy。

Introduction to the software and hardware was done inpidually to ensure that every student gets the chance to learn the software and become equipped to work independently。 However, collaborative learning was used for the coursework and possible teams were made of students of different courses or degrees to achieve interdisciplinary group work。 Although, this approach with respect to a more theoretical one, has affected student learning in a positive way, students found the task challenging, especially those who did not take the control systems module。 However, they found the practical aspects of the course the most interesting ones and a better way of relating theory to practice。

In the future, we intend to use the robotic platforms in our Mechatronics, Controls and Embedded systems courses to demonstrate control concepts and allow the students to implement different advanced control strategies。 Moreover, students will receive formal lectures in control systems and fuzzy logic before they are asked to program and create the embedded implementation as part of a new module, namely “control systems and embedded implementation”。

7。CONCLUSIONS

The outcomes of the development and deployment of the materials lay within communication and research skills which are fundamental for engineers to be able to work on teams (i。e。 to gather data, understand it and reach conclusions)。 The work presented here has been research informed by the work of a PhD candidate and delivers a range of robotics and control concepts, and control strategies, including fuzzy logic and PID controllers, and state estimation based on a Kalman filter。 Control strategies for adaptive cruise control (ACC) based on a fuzzy PID controller are proposed designed, implemented and deployed in a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit)。 The methodology is based on four main steps: 1) system modelling and identification, 2) control tuning and designing for the ACC system, 3) implementation and testing in the simulation, and 4) deploying to the hardware。 System identification is achieved by means of LabVIEW。 Simulation and real implementation are compared showing a very good correlation。 Furthermore, implementation of the fuzzy PID controller shows up to 60% error reduction compared to standard PID controller。 Future work will deal with other types of control algorithms (e。g。 Model Predictive Control) and hardware enhancement/expansion, to include further sensors, for example global positioning systems (GPS),  inertial navigation systems (INS), vision systems etc。

Fig。 7。 Testing the ACC system utilising two test robots- the distance measured by an ultrasonic sensor and unmeasured parameters are estimated by a Kalman filter: (a) the inter- distance between the robots, (b) the velocity of the leading and following robots during tracking, and (c) the estimated acceleration of the following robot by a Kalman filter。

REFERENCES

Brew, A。 (2003)。 Teaching and research: New relationships and their implications for inquiry-based teaching and learning in higher education。 Higher Education Research and Development 22 (1) 3-18。