廖哲霖, 彭建伟, 姚瀚晨, 苏泽凡, 戴厚德. 基于导纳控制的社交机器人伴随与避障控制策略[J]. 机器人, 2024, 46(3): 305-316. DOI: 10.13973/j.cnki.robot.230163
引用本文: 廖哲霖, 彭建伟, 姚瀚晨, 苏泽凡, 戴厚德. 基于导纳控制的社交机器人伴随与避障控制策略[J]. 机器人, 2024, 46(3): 305-316. DOI: 10.13973/j.cnki.robot.230163
LIAO Zhelin, PENG Jianwei, YAO Hanchen, SU Zefan, DAI Houde. Admittance Control-based Human-accompanying and Obstacle-avoidance Control Strategy for Social Robot[J]. ROBOT, 2024, 46(3): 305-316. DOI: 10.13973/j.cnki.robot.230163
Citation: LIAO Zhelin, PENG Jianwei, YAO Hanchen, SU Zefan, DAI Houde. Admittance Control-based Human-accompanying and Obstacle-avoidance Control Strategy for Social Robot[J]. ROBOT, 2024, 46(3): 305-316. DOI: 10.13973/j.cnki.robot.230163

基于导纳控制的社交机器人伴随与避障控制策略

Admittance Control-based Human-accompanying and Obstacle-avoidance Control Strategy for Social Robot

  • 摘要: 目前社交机器人与目标人体并排行走(人体伴随与避障)时的运动控制策略的研究相对缺乏。为提升陪伴任务中用户的舒适度、机器人的运动柔顺度及其安全避障能力,本文提出了一种基于导纳控制的人体伴随与避障控制策略。首先,基于人机交互空间理论设计了交互力模型以构建人机动态交互关系,避免机器人侵犯目标人的亲密区域以提升目标人的舒适度;其次,将导纳控制模型与交互力模型结合,通过设置合理的导纳参数提升机器人运动控制的柔顺度;最后,引入行为动力学模型模拟人类的避障行为,以保障人体伴随任务的安全性。此外,提出了一组评价指标以验证伴随控制器的性能。根据仿真实验结果,在柔顺度方面,相较于PID法和虚拟弹簧模型(VSM)法,本方法下速度变化量分别降低69.6% 和67.1%;在舒适度方面,机器人未给目标人带来不适;在安全性方面,本方法的避障失败率仅为10%,优于人工势场(APF)法和VSM法的40% 和50%。实物实验中,机器人的柔顺度和舒适度指标均较好,避障失败率仅为5%,有效实现了安全友好的人体伴随与避障控制。

     

    Abstract: Currant research on the motion control strategies of the social robots walking side-by-side with a target human body (i.e. human accompanying and obstacle avoidance) is deficient. To improve the accompanying comfort level, and the motion compliance and safe obstacle-avoidance performances of the robot, a human-accompanying and obstacle-avoidance control strategy is proposed based on admittance control. Firstly, an interaction force model is designed based on the theory of human-robot interaction space, which describes the dynamic human-robot interaction relationship to prevent the robot from infringing into the companion's intimate area, thereby enhancing the comfort level of the companion. Secondly, the admittance control model is combined with the interaction force model to improve the motion compliance of the robot through optimal admittance parameters. Finally, a behavioral dynamics model is introduced to simulate the human obstacle-avoidance behavior, thereby ensuring the safety of the human-accompanying task. Additionally, a set of evaluation indexes are proposed for human-accompanying performance. The simulation results show that the robot velocity change under the proposed method is reduced by 69.6% and 67.1% respectively compared with PID (proportional-integral-derivative) and VSM (virtual spring model) methods, demonstrating its advantages in terms of compliance; in terms of comfort, the robot doesn't cause discomfort to humans; in terms of safety, the obstacle-avoidance failure rate of the proposed method is only 10%, better than the 40% and 50% of APF (artificial potential field) and VSM methods. In the physical experiment, the robot exhibits better compliance and comfort performances, and the obstacle-avoidance failure rate is only 5%. Therefore, the proposed method effectively achieves safe and friendly human accompanying and obstacle-avoidance control.

     

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