Autonomous driving paper index
An experiential learning framework for teaching non-holonomic mobile robot kinematics and control using a ROS 2-enabled differential drive platform
One-line summary
In senior-level robotics courses, students often struggle to connect analytical models of differential drive kinematics and control with the behavior of real mobile robots, particularly when non-holonomic constraints and ROS2 based software architectures are involved.
Engineering notes
Key topics: autonomous driving, deployment, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In senior-level robotics courses, students often struggle to connect analytical models of differential drive kinematics and control with the behavior of real mobile robots, particularly when non-holonomic constraints and ROS2 based software architectures are involved. This article presents an experiential learning framework for teaching non-holonomic mobile robot kinematics, odometry, and closed-loop control using a ROS2 enabled Ati Sherpa RP differential drive platform. The four-week module combines platform-specific derivation of inverse and forward Jacobians with simulation-based validation in IR Sim and subsequent deployment to physical hardware. The framework was implemented with final-year mechanical engineering students, and its educational effectiveness was evaluated using pre-/post-quizzes, rubric-based assessment of simulation and hardware exercises, and a post-module survey. Results show marked conceptual gains (pre-quiz mean 46% vs. post-quiz mean 81%, Cohen’s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2.7</mml:mn> </mml:math> ) in students’ ability to derive and interpret differential drive kinematics, implement Jacobian-based motion models, and tune PI controllers, with gains concentrated on the hardest conceptual items involving non-holonomic constraints. Hardware deployment success (9 of 10 teams within the specified error bounds) further corroborates the practical effectiveness of the framework. Students reported increased confidence in applying kinematic models and valued the transparent linkage between equations, code, and robot motion. The proposed framework offers a reproducible, ROS2-compatible approach for aligning mathematical theory, software implementation, and authentic laboratory practice in mechanical engineering education.
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