面向农林巡检机器人的《机器人操作系统》PBL教学实践

PBL Teaching Practice in the “Robot Operating System” Course for Agricultural and Forestry Inspection Robots

摘要: 针对新工科背景下《机器人操作系统》(ROS)课程存在的知识碎片化、理论与实践脱节、学生内驱力不足等痛点，本文基于成果导向教育(OBE)与项目驱动学习(PBL)理念，构建了一套面向真实工程需求的理论与项目实践深度融合教学模式。该模式以“农林巡检机器人”项目为唯一载体，将ROS课程的全部核心知识点重构为贯穿始终的功能子任务群，并依托NVIDIA Jetson AGX Xavier嵌入式高性能平台进行实战部署与系统集成。同时，本文创新性地引入竞赛式评估体系，将过程性考核与最终项目性能竞赛相结合，通过高低分排序机制将项目排名与期末成绩直接挂钩，以强激励机制驱动学生追求系统性能最优。教学实践证明，该融合模式显著增强了学生的系统集成能力、复杂问题解决能力和学习主动性，为培养符合智能制造产业需求的复合型机器人工程人才提供了有效途径。

Abstract: Addressing the pain points of the “Robot Operating System” (ROS) course under the new engineering background, such as fragmented knowledge, disconnect between theory and practice, and insufficient student motivation, this paper constructs a teaching model that deeply integrates theory and project practice based on the concepts of Outcome-Based Education (OBE) and Project-Driven Learning (PBL). This model uses the “Agricultural and Forestry Inspection Robot” project as the sole vehicle, reconstructing all core knowledge points of the ROS course into a group of functional sub-tasks throughout the course, and relying on the NVIDIA Jetson AGX Xavier embedded high-performance platform for practical deployment and system integration. Simultaneously, this paper innovatively introduces a competition-style evaluation system, combining process assessment with a final project performance competition. A high-score ranking mechanism directly links project ranking with final grades, driving students to pursue optimal system performance through a strong incentive mechanism. Teaching practice has proven that this integrated model significantly enhances students’ system integration capabilities, complex problem-solving abilities, and learning initiative, providing an effective approach to cultivating interdisciplinary robotics engineering talents who meet the needs of the intelligent manufacturing industry.