“智能机器人技术”课程教学改革实践

为解决高校智能机器人工程专业在教学实践环节中教学定位不明确、缺乏规范的课程标准与教学模式、教学评价指标不够全面等问题,从机器人工程专业学科的综合性和实践性强、交互性和探究性强的教学特点入手,以多类型移动智能机器人平台为对象,围绕机器人本体结构设计、运动控制与作业系统的仿真与工程验证,构建以电子电路设计—控制软件—离线与系统仿真-项目实践为一体的学习框架,实现《智能机器人技术》从认知型教学到应用型教学,再到研究型教学的贯通式和项目导向式教学。实践表明,改革后的《智能机器人技术》课程的教学内容和方法保证了理论教学与实践教学的先进性,提升了学生的专业水平,培养了学生的项目经验和工程应用能力,提高了学生的科学素养和综合能力。     

A robust collision prediction and detection method based on neural network for autonomous delivery robots

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.