Research On Trajectory Tracking And Path Planning Algorithm Of Dsp-based Educational Robot

Mobile robot is an integrative subject with updated research results in mechanical, electronic, computer, automatic control, and artificial intelligence, represents tremendous success of mechatronics, as an important branch of the field of robotics is becoming an important research area in automation, computer, and artifieial intelligene. Educational robot is one type of intelligent robot devoted to the field of education, as full of technology and interestingness is becoming a trainning method for electronic information, automation and other areas of technology professionals, then educational robot will come into college, as teaching content of a specialized discipline, as a support of innovative programs, as an research platform of information discipline.The goal of the project is to develop a modular multi-function as a learning, research educational robot platform. First, an overview of domestic and international situation, then given a overall learning platform plan based on educational robot aim to innovate engineering education , integrated electronic and information engineering curriculum based on the learning platform. Then, designed the low-intelligent reactive tracking system of the DSP-based educational robot, the tracking test results of the robot met expectations.To meet the need to the depth of student learning and development of intelligent educational robot, the trajectory tracking and path planning algorithm is studied. Analyzed kinematics and tracking model of educational robot, Based on Backstepping algorithm using the angular velocity of the two wheel drive as input designed a new trajectory tracking controller, and simulation results show the effectiveness of the new controller, apply to global trajectory tracking for robot. For global path planning of educational robot based on ant colony algorithm, proposed to improve the shortcomings and deficiencies of the ant colony algorithm in these three sides of path selection strategy, pheromone update strategy, heuristic information update strategy, analyzed the convergence of the improved algorithms, then simulation and comparison to Ant-cycle model results show the effectiveness of the inproved algorithms.