轮式机器人目前已经在多个领域得到了应用,但机器人在轨迹跟踪控制中存在数据参数分析和轨迹跟踪控制效果不佳的问题。为了提升机器人移动过程中的轨迹跟踪控制效果,采用差分进化(Differential Evolution,DE)对灰狼优化(Grey Wolf Optim...轮式机器人目前已经在多个领域得到了应用,但机器人在轨迹跟踪控制中存在数据参数分析和轨迹跟踪控制效果不佳的问题。为了提升机器人移动过程中的轨迹跟踪控制效果,采用差分进化(Differential Evolution,DE)对灰狼优化(Grey Wolf Optimizer,GWO)算法进行改进,通过改进收敛因子和交叉变异过程,提升了机器人控制效果。实验结果表明,改进算法在机器人控制中适配度最低值为0.04,比遗传算法的适配度降低了0.88。改进算法的比例值为10.8469,积分值为2.3548,微分值为0.0654。实际应用和测试结果表明该方法对机器人轨迹跟踪控制有较好的效果。展开更多
To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various ter...To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However,less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.展开更多
近年来,模仿学习被广泛应用于机器人领域,并展示出巨大的潜力。同时关注到智能系统在教育领域的应用越来越多样化,将机器人合理地应用到教学中可以提升教学效果,如果机器人可以教授一些专业技巧,如演奏乐器,可以为学生和人类老师都提供...近年来,模仿学习被广泛应用于机器人领域,并展示出巨大的潜力。同时关注到智能系统在教育领域的应用越来越多样化,将机器人合理地应用到教学中可以提升教学效果,如果机器人可以教授一些专业技巧,如演奏乐器,可以为学生和人类老师都提供很大的便利。模仿学习特别适用于高度专业和技术性强的小提琴演奏,但在将专家演示引入动态运动原语(Dynamic Movement Primitive,DMP)的过程中,模糊性问题尤为突出,例如换弦角度的不确定性。传统的换弦角度测量方法如物理测量会有很大的误差且无法泛化,为了解决这一问题,提出了一种名为基于模糊和PCA的动态运动原语(Fuzzy Dynamic Movement Primitive for Teaching,T-FDMP)的新模型。该模型基于二型模糊模型和主成分分析(Principal Component Analysis,PCA)进行构建,使用主成分分析法(PCA)得到的特征变量(运弓角度)作为隶属度函数(琴弦角度)的输入进行学习,同时构建了一个专业级的音乐演奏行为数据库。仿生实验结果证明,所提出的T-FDMP模型能够以高精度控制机器人进行小提琴演奏,还为模仿学习在其他高度专业和技术性强的领域的应用提供了新的研究方向。展开更多
文摘轮式机器人目前已经在多个领域得到了应用,但机器人在轨迹跟踪控制中存在数据参数分析和轨迹跟踪控制效果不佳的问题。为了提升机器人移动过程中的轨迹跟踪控制效果,采用差分进化(Differential Evolution,DE)对灰狼优化(Grey Wolf Optimizer,GWO)算法进行改进,通过改进收敛因子和交叉变异过程,提升了机器人控制效果。实验结果表明,改进算法在机器人控制中适配度最低值为0.04,比遗传算法的适配度降低了0.88。改进算法的比例值为10.8469,积分值为2.3548,微分值为0.0654。实际应用和测试结果表明该方法对机器人轨迹跟踪控制有较好的效果。
文摘To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However,less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.
文摘近年来,模仿学习被广泛应用于机器人领域,并展示出巨大的潜力。同时关注到智能系统在教育领域的应用越来越多样化,将机器人合理地应用到教学中可以提升教学效果,如果机器人可以教授一些专业技巧,如演奏乐器,可以为学生和人类老师都提供很大的便利。模仿学习特别适用于高度专业和技术性强的小提琴演奏,但在将专家演示引入动态运动原语(Dynamic Movement Primitive,DMP)的过程中,模糊性问题尤为突出,例如换弦角度的不确定性。传统的换弦角度测量方法如物理测量会有很大的误差且无法泛化,为了解决这一问题,提出了一种名为基于模糊和PCA的动态运动原语(Fuzzy Dynamic Movement Primitive for Teaching,T-FDMP)的新模型。该模型基于二型模糊模型和主成分分析(Principal Component Analysis,PCA)进行构建,使用主成分分析法(PCA)得到的特征变量(运弓角度)作为隶属度函数(琴弦角度)的输入进行学习,同时构建了一个专业级的音乐演奏行为数据库。仿生实验结果证明,所提出的T-FDMP模型能够以高精度控制机器人进行小提琴演奏,还为模仿学习在其他高度专业和技术性强的领域的应用提供了新的研究方向。
