This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior ...This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.展开更多
Based upon the theory of the nonlinear quadric two-person nonzero-sum differential game,the fact that the time-limited mixed H2/H∞ control problem can be turned into the problem of solving the state feedback Nash bal...Based upon the theory of the nonlinear quadric two-person nonzero-sum differential game,the fact that the time-limited mixed H2/H∞ control problem can be turned into the problem of solving the state feedback Nash balance point is mentioned. Upon this,a theorem about the solution of the state feedback control is given,the Lyapunov stabilization of the nonlinear system under this control is proved,too. At the same time,this solution is used to design the nonlinear H2/H∞ guidance law of the relative motion between the missile and the target in three-dimensional(3D) space. By solving two coupled Hamilton-Jacobi partial differential inequalities(HJPDI),a control with more robust stabilities and more robust performances is obtained. With different H∞ performance indexes,the correlative weighting factors of the control are analytically designed. At last,simulations under different robust performance indexes and under different initial conditions and under the cases of intercepting different maneuvering targets are carried out. All results indicate that the designed law is valid.展开更多
目的探讨个性化咬合诱导矫治器治疗儿童安氏Ⅱ类1分类患儿的临床疗效。方法选取2020年6月至2022年5月于合肥市口腔医院就诊的儿童安氏Ⅱ类1分类患儿60例,按随机数字表法将治疗组分为个性化咬合诱导矫治器组(罗慕组)和传统功能矫正器组(T...目的探讨个性化咬合诱导矫治器治疗儿童安氏Ⅱ类1分类患儿的临床疗效。方法选取2020年6月至2022年5月于合肥市口腔医院就诊的儿童安氏Ⅱ类1分类患儿60例,按随机数字表法将治疗组分为个性化咬合诱导矫治器组(罗慕组)和传统功能矫正器组(TB组),每组各30例。收集同期30例安氏Ⅱ类1分类但未参与早期矫治的儿童为对照组,随访观察。应用逆向工程软件(Mimics Research 21.0)、Dolphin Imaging软件测量3组患者治疗(观察)前和治疗(观察)12个月后的下颌骨、上下牙弓及头颅侧位片中各项指标。结果罗慕组治疗12个月后较治疗前,下颌升支高度(Co-Go)、下颌基骨长度(Go-Po)、上牙弓宽度(UR6-UL6)、下牙弓宽度(LR6-LL6)、下齿槽座角(SNB)增加,上下齿槽座角(ANB)、上中切牙倾斜度(U1-NA)、覆合、覆盖减小,差异有统计学意义(P<0.05)。TB组治疗12个月后较治疗前,Co-Go、Go-Po、UR6-UL6、SNB、下颌平面角(FMA)、下颌角(Ar-Go-Me)、下颌中切牙倾斜度(L1-NB)增加,ANB、U1-NA、覆合、覆盖减小,差异有统计学意义(P<0.05)。3组患者治疗前后差值比较,LR6-LL6、FMA、L1-NB等差异有统计学意义(P<0.05)。治疗后,罗慕组主观性评分高于TB组(P<0.05)。结论个性化咬合诱导矫治器可以矫治儿童安氏Ⅱ类1分类错合畸形,同时有改善下牙弓宽度,控制下颌平面角和前牙唇倾的作用。展开更多
文摘This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.
基金Sponsored by the National Natural Science Foundation of China (Grant No.90716028)
文摘Based upon the theory of the nonlinear quadric two-person nonzero-sum differential game,the fact that the time-limited mixed H2/H∞ control problem can be turned into the problem of solving the state feedback Nash balance point is mentioned. Upon this,a theorem about the solution of the state feedback control is given,the Lyapunov stabilization of the nonlinear system under this control is proved,too. At the same time,this solution is used to design the nonlinear H2/H∞ guidance law of the relative motion between the missile and the target in three-dimensional(3D) space. By solving two coupled Hamilton-Jacobi partial differential inequalities(HJPDI),a control with more robust stabilities and more robust performances is obtained. With different H∞ performance indexes,the correlative weighting factors of the control are analytically designed. At last,simulations under different robust performance indexes and under different initial conditions and under the cases of intercepting different maneuvering targets are carried out. All results indicate that the designed law is valid.
文摘目的探讨个性化咬合诱导矫治器治疗儿童安氏Ⅱ类1分类患儿的临床疗效。方法选取2020年6月至2022年5月于合肥市口腔医院就诊的儿童安氏Ⅱ类1分类患儿60例,按随机数字表法将治疗组分为个性化咬合诱导矫治器组(罗慕组)和传统功能矫正器组(TB组),每组各30例。收集同期30例安氏Ⅱ类1分类但未参与早期矫治的儿童为对照组,随访观察。应用逆向工程软件(Mimics Research 21.0)、Dolphin Imaging软件测量3组患者治疗(观察)前和治疗(观察)12个月后的下颌骨、上下牙弓及头颅侧位片中各项指标。结果罗慕组治疗12个月后较治疗前,下颌升支高度(Co-Go)、下颌基骨长度(Go-Po)、上牙弓宽度(UR6-UL6)、下牙弓宽度(LR6-LL6)、下齿槽座角(SNB)增加,上下齿槽座角(ANB)、上中切牙倾斜度(U1-NA)、覆合、覆盖减小,差异有统计学意义(P<0.05)。TB组治疗12个月后较治疗前,Co-Go、Go-Po、UR6-UL6、SNB、下颌平面角(FMA)、下颌角(Ar-Go-Me)、下颌中切牙倾斜度(L1-NB)增加,ANB、U1-NA、覆合、覆盖减小,差异有统计学意义(P<0.05)。3组患者治疗前后差值比较,LR6-LL6、FMA、L1-NB等差异有统计学意义(P<0.05)。治疗后,罗慕组主观性评分高于TB组(P<0.05)。结论个性化咬合诱导矫治器可以矫治儿童安氏Ⅱ类1分类错合畸形,同时有改善下牙弓宽度,控制下颌平面角和前牙唇倾的作用。
