A trajectory generation method which is based on NURBS interpolation is studied to improve the fitting accuracy and smoothness of non-circular cross section and obtain higher accuracy of the final non-circular profile...A trajectory generation method which is based on NURBS interpolation is studied to improve the fitting accuracy and smoothness of non-circular cross section and obtain higher accuracy of the final non-circular profile control. After using the NURBS, the most optimized and smooth trajectory for the linear actuator can be obtained. For the purpose of machining the non-circular cross section by CNC turning, the fast response linear actuator has been used. The control algorithm which is compound control of proportional-integral-differential （PID） and iterative learning control has been developed for non-circular profile generation. By using the NURBS interpolation and the compound control of PID and iterative learning control, the final motion accuracy of linear actuator has been improved, therefore, the machining accuracy of the non-circular turning can be improved.展开更多
Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features...Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.展开更多
With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Curren...With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.展开更多
基金the Tenth Five-Year National Science and Technology Key Project of China(No.BA203B04).
文摘A trajectory generation method which is based on NURBS interpolation is studied to improve the fitting accuracy and smoothness of non-circular cross section and obtain higher accuracy of the final non-circular profile control. After using the NURBS, the most optimized and smooth trajectory for the linear actuator can be obtained. For the purpose of machining the non-circular cross section by CNC turning, the fast response linear actuator has been used. The control algorithm which is compound control of proportional-integral-differential （PID） and iterative learning control has been developed for non-circular profile generation. By using the NURBS interpolation and the compound control of PID and iterative learning control, the final motion accuracy of linear actuator has been improved, therefore, the machining accuracy of the non-circular turning can be improved.
基金supported by European Community s Seventh Framework Programme FP7/2007-2013,Challenge 2,Cognitive Systems,Interaction,Robotics(No.248311AMARSi)
文摘Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.
基金supported by the National Natural Science Foundation of China(Grant No.61772249).
文摘With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.
