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Introduction A good comprehension of the autonomous robotics field heavily relies on solid background knowledge in a great variety of fields like mathematics, probability, physics, mechanics, electronics, and computer science.
A good undergraduate level introductory robotics course should cover as many fun- damental topics as possible, yet should be easy to understand.
Additionally each covered topic should be backed up by an appropriate hands-on labo- ratory session for providing a concrete experience on the application of the covered theoretical material.
Teaching robotics to computer science under- graduate students is a challenging problem since they are used to develop software for ready to use hardware and hardly know about the uncertainty associated with every physical part of a typical robotic system. An evaluation of the course from the student point of view is also presented.
Course Description Based on our previous teaching experiences in graduate level introductory robotics courses, we started with the following main groups of topics that we consider important and would like to cover in the course: The lab sessions associated with the covered topics can be listed as follows: The main purpose of this lab is to become familiar with the Lego parts and the development environment.
Building a simple robot and implementing a set of simple controllers using the Lego Mindstorms software. Converting firmwares of NXT bricks for Java compatibility and reimplementing previously implemented con- trollers using Java. The students are expected to calibrate the light, ultrasound, sound, and touch sensors, and also the motors by fitting a model on the relation between the measured property and the sensory reading. A similar procedure is repeated for es- tablishing the relation between the motor speeds applied and the linear and rotational motion observed.
Application of UMBmark odometry calibration pro- cedure9 on differential drive robots and reporting of the observed errors as well as the level of improvement before and after calibra- tion.
The students are asked to build a two wheeled self- balancing robot using a light sensor pointing downwards as its feed- back mechanism.
They then try to find appropriate coefficients for a complete PID control code for keeping the robot balanced. The aim of this lab is to show the stu- dents that complex behaviors can be obtained by using very primi- tive sensory-motor couplings.
The students build a pre- designed robot with an ultrasonic sensor on a turning turret and then they are asked to develop a behavior scheme using subsump- tion architecture11 for seeking a light source and heading the robot towards it. This lab mainly concentrates on developing appro- priate observation and motion models for a complete Monte-Carlo Localization system.
The students implement the Bug1 algorithm13 for a robot that tries to avoid obstacles and run towards a light source.
Term Project The term project includes the simplified versions of the main problems in the field of robotics, some of which are path planning, map making, perceiv- ing the environment, and odometry. In addition, the mechanical design for a pole climbing robot is a part of the project to challenge the physical capa- bilities of the robot.
The students are asked to design a robot for rescuing the princess from a dungeon.
The robot needs to climb a pole after finding the princess in order to get out of the dungeon. The dungeon world is implemented as a rectangular area consisting of square cells and the objects of interest are color-coded. The area is surrounded by walls and each square can contain at most one type of object.
The valid object set consists of: The initial position of the robot is determined randomly and is same for all student groups. Figure 1 shows the picture of two models designed for the term project. Two models for the specified tasks.
Student Evaluation This project was very beneficial for giving us the chance of designing and implementing algorithms for some of the main problems of robotics, such as perceiving the environment, map making, and calibrating and making use of odometry, and path planning. The limitation on the number of motors challenged us to come up with clever designs for pole climbing.
The boundaries of the map are known by the robot.
However, the start- ing location or the locations of the obstacles on the map are not given, so the robot has to explore. The robot is supposed to know what the perceived cell contains. This is achieved by using the light sensors since the colors of the goal and the pole is specified in the description.
The agent should know the cells it has visited before in order to prevent deadlocks and to be able to find the goal. Since the world is defined as discrete cells and the odometry readings supply reasonably deterministic displacement feedback, this requirement can be considered as less complex than a regular map making problem.
The robot is supposed to know the content of the cells in its perception. The light sensors are utilized for that purpose, since the colors of the goal and the pole is specified in the description and although the light sensors of Lego are only sensitive to intensity, intensity characteristics of different colors can be modeled and distinguished statistically. The Lego brick that the robot carries is too heavy to lift; therefore, the robot can slide while climbing.
In order to prevent that, feedback from the motors are considered and the force to be applied is determined accordingly using a control loop. The software on the robot is mainly responsible for decision making part.
A variety of possible misperceptions are considered and reasoning is done by taking those situations into account. The experience gained in the laboratory sessions of the course had an important role to implement an efficient framework since it is important to be familiar with the capabilities of both software and hardware parts of the robot, and to know what it is capable of doing as well as what it is not.
Touch Sensor. Remote Infrared Beacon. IR Sensor. Large motor x2.
Medium Motor. And much more Need further help? Build a robot. Learn to program. About EV3. Robot Commander App.