This paper studies the problem of coordinated motion generation for a group of rigid bodies. Two classes of coordinated motion primitives, relative equilibria and ma- neuvers, are given as building blocks for generati...This paper studies the problem of coordinated motion generation for a group of rigid bodies. Two classes of coordinated motion primitives, relative equilibria and ma- neuvers, are given as building blocks for generating coordi- nated motions. In a motion-primitive based planning frame- work, a control method is proposed for the robust execution of a coordinated motion plan in the presence of perturba- tions. The control method combines the relative equilibria stabilization with maneuver design, and results in a close- loop motion planning framework. The performance of the control method has been illustrated through a numerical sim- ulation.展开更多
Human-robot collaboration fully leverages the strengths of both humans and robots,which is crucial for handling large,heavy objects at construction sites.To address the challenges of human-machine cooperation in handl...Human-robot collaboration fully leverages the strengths of both humans and robots,which is crucial for handling large,heavy objects at construction sites.To address the challenges of human-machine cooperation in handling large-scale,heavy objects-specifically building curtain walls-a human-robot collaboration system was designed based on the concept of"human-centered with machine support".This system allows the handling of curtain walls according to different human intentions.First,a robot trajectory learning and generalization model based on dynamic motion primitives was developed.The operator's motion intent was then characterized by their speed,force,and torque,with the force impulse introduced to define the operator's intentions for acceleration and deceleration.Finally,a collaborative experiment was conducted on an experimental platform to validate the robot's understanding of human handling intentions and to verify its ability to handle curtain wall.Collaboration between humans and robots ensured a smooth and labor-saving handling process.展开更多
To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framew...To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.展开更多
Let { W(t);t≥0 } be a standard Brownian motion.For a positive integer m ,define a Gaussian processX m(t)=1m!∫ t 0(t-s) m d W(s).In this paper the liminf behavior of the increments of this process is discu...Let { W(t);t≥0 } be a standard Brownian motion.For a positive integer m ,define a Gaussian processX m(t)=1m!∫ t 0(t-s) m d W(s).In this paper the liminf behavior of the increments of this process is discussed by establishing some probability inequalities.Some previous results are extended and improved.展开更多
Segmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment....Segmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment.With the aim to conveniently and accurately segment demonstration trajectories,a novel demonstration trajectory segmentation approach is proposed based on the beta process autoregressive hidden Markov model(BP-ARHMM)algorithm and generalised time warping(GTW)algorithm aiming to enhance the segmentation accuracy utilising acquired demonstration data.This approach first adopts the GTW algorithm to align the multiple demonstration trajectories for the same task.Then,it adopts the BP-AR-HMM algorithm to segment the demonstration trajectories,acquire the contained motion primitives,and establish the related task library.This segmentation approach is validated on the 6-degree-of-freedom JACO robotic arm by assisting users to accomplish a holding water glass task and an eating task.The experimental results show that the motion primitives within the trajectories can be correctly segmented with a high segmentation accuracy.展开更多
An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and...An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.展开更多
A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural langu...A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.展开更多
A methodology is proposed to enable real-time evaluation of the observability of local motions,and generate a local observability cost map to enable informed local motion planning in order to avoid potential degradati...A methodology is proposed to enable real-time evaluation of the observability of local motions,and generate a local observability cost map to enable informed local motion planning in order to avoid potential degradation or degeneracy in state estimator performance.The proposed approach leverages efficient numerical techniques in nonlinear observability analysis and motion primitive-based planning technique to realize the local observability prediction with real-time performance.The degradation of the state estimation performance can be readily predicted with the local observability evaluation result.The proposed approach is specialized to a representative optimization-based monocular visual-inertial state estimation formulation and evaluated through simulation and experiments.The experimental results demonstrated the ability of the proposed methodology to correctly anticipate the potential state estimation degradation.展开更多
In order to ensure the safety and efficiency of planetary exploration rovers,path planning and tracking control of a planetary rover are expected to consider factors such as complex 3D terrain features,the motion cons...In order to ensure the safety and efficiency of planetary exploration rovers,path planning and tracking control of a planetary rover are expected to consider factors such as complex 3D terrain features,the motion constraints of the rover,traversability,etc.An improved path planning and tracking control method is proposed for planetary exploration rovers on rough terrain in this paper.Firstly,the kinematic model of the planetary rover is established.A 3D motion primitives library adapted to various terrains and the rover’s orientations is generated.The state expansion process and heuristic function of the A*algorithm are improved using the motion primitives and terrain features.Global path is generated by improved A*-based algorithm that satisfies the planetary rover’s kinematic constraints and the 3D terrain restrictions.Subsequently,an optional arc path set is designed based on the traversable capabilities of the planetary rover.Each arc path corresponds to a specific motion that determines the linear and angular velocities of the planetary rover.The optimal path is selected through the multi-objective evaluation function.The planetary rover is driven to accurately track the global path by sending optimal commands that corresponds to the optimal path for real-time obstacle avoidance.Finally,the path planning and tracking control method is effectively validated during a given mission through two simulation tests.The experiment results show that the improved A*-based algorithm reduces planning time by 30.05% and generates smoother paths than the classic A^(*) algorithm.The multi-objective arc-based method improves the rover’s motion efficiency,ensuring safer and quicker mission completion along the global path.展开更多
Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a g...Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption.A model predictive control(MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning.First,the motion primitives of each quadrotor are formulated as the boundary state constrained primitives(BSCPs)which are constructed with jerk limited trajectory(JLT)generation method,a boundary value problem(BVP)solver,to obtain time-optimal trajectories.They are then approximated with a neural network(NN),pre-trained using this solver to reduce the computational burden.The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee°ight safety without deadlock.Finally,the reference trajectories are generated using the same BVP solver.Our simulation and experimental results demonstrate the superior performance of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China (11072002,10832006)
文摘This paper studies the problem of coordinated motion generation for a group of rigid bodies. Two classes of coordinated motion primitives, relative equilibria and ma- neuvers, are given as building blocks for generating coordi- nated motions. In a motion-primitive based planning frame- work, a control method is proposed for the robust execution of a coordinated motion plan in the presence of perturba- tions. The control method combines the relative equilibria stabilization with maneuver design, and results in a close- loop motion planning framework. The performance of the control method has been illustrated through a numerical sim- ulation.
基金supported by 2022 Doctoral Fund Project of Shandong Jianzhu University(X22012Z)the National Natural Science Foundation of China(U20A20283).
文摘Human-robot collaboration fully leverages the strengths of both humans and robots,which is crucial for handling large,heavy objects at construction sites.To address the challenges of human-machine cooperation in handling large-scale,heavy objects-specifically building curtain walls-a human-robot collaboration system was designed based on the concept of"human-centered with machine support".This system allows the handling of curtain walls according to different human intentions.First,a robot trajectory learning and generalization model based on dynamic motion primitives was developed.The operator's motion intent was then characterized by their speed,force,and torque,with the force impulse introduced to define the operator's intentions for acceleration and deceleration.Finally,a collaborative experiment was conducted on an experimental platform to validate the robot's understanding of human handling intentions and to verify its ability to handle curtain wall.Collaboration between humans and robots ensured a smooth and labor-saving handling process.
基金Supported by National Natural Science Foundation of China (Grant Nos. 91420203 and 61703041)。
文摘To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.
基金Project Supported by National Science Fundation of China(1 9571 0 2 1 ) and Zhejiang Province
文摘Let { W(t);t≥0 } be a standard Brownian motion.For a positive integer m ,define a Gaussian processX m(t)=1m!∫ t 0(t-s) m d W(s).In this paper the liminf behavior of the increments of this process is discussed by establishing some probability inequalities.Some previous results are extended and improved.
基金Doctoral Research Start-up Fund of Shandong Jiaotong University,Grant/Award Number:BS2024009Natural Science Foundation of Shandong Province of China,Grant/Award Number:ZR2022ME087+1 种基金State Key Laboratory of Robotics and Systems(HIT),Grant/Award Number:SKLRS-2024-KF-09Open Access Publication Fund of Universität Hamburg。
文摘Segmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment.With the aim to conveniently and accurately segment demonstration trajectories,a novel demonstration trajectory segmentation approach is proposed based on the beta process autoregressive hidden Markov model(BP-ARHMM)algorithm and generalised time warping(GTW)algorithm aiming to enhance the segmentation accuracy utilising acquired demonstration data.This approach first adopts the GTW algorithm to align the multiple demonstration trajectories for the same task.Then,it adopts the BP-AR-HMM algorithm to segment the demonstration trajectories,acquire the contained motion primitives,and establish the related task library.This segmentation approach is validated on the 6-degree-of-freedom JACO robotic arm by assisting users to accomplish a holding water glass task and an eating task.The experimental results show that the motion primitives within the trajectories can be correctly segmented with a high segmentation accuracy.
文摘An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.
基金This work was partially supported by the Royal Society of UK and the National Natural Science Foundation of PRC (No. 60175028).
文摘A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.
文摘A methodology is proposed to enable real-time evaluation of the observability of local motions,and generate a local observability cost map to enable informed local motion planning in order to avoid potential degradation or degeneracy in state estimator performance.The proposed approach leverages efficient numerical techniques in nonlinear observability analysis and motion primitive-based planning technique to realize the local observability prediction with real-time performance.The degradation of the state estimation performance can be readily predicted with the local observability evaluation result.The proposed approach is specialized to a representative optimization-based monocular visual-inertial state estimation formulation and evaluated through simulation and experiments.The experimental results demonstrated the ability of the proposed methodology to correctly anticipate the potential state estimation degradation.
基金funded by the State Key Laboratory,China(KJW6142210210308)the National Natural Science Founda-tion of China(61806183).
文摘In order to ensure the safety and efficiency of planetary exploration rovers,path planning and tracking control of a planetary rover are expected to consider factors such as complex 3D terrain features,the motion constraints of the rover,traversability,etc.An improved path planning and tracking control method is proposed for planetary exploration rovers on rough terrain in this paper.Firstly,the kinematic model of the planetary rover is established.A 3D motion primitives library adapted to various terrains and the rover’s orientations is generated.The state expansion process and heuristic function of the A*algorithm are improved using the motion primitives and terrain features.Global path is generated by improved A*-based algorithm that satisfies the planetary rover’s kinematic constraints and the 3D terrain restrictions.Subsequently,an optional arc path set is designed based on the traversable capabilities of the planetary rover.Each arc path corresponds to a specific motion that determines the linear and angular velocities of the planetary rover.The optimal path is selected through the multi-objective evaluation function.The planetary rover is driven to accurately track the global path by sending optimal commands that corresponds to the optimal path for real-time obstacle avoidance.Finally,the path planning and tracking control method is effectively validated during a given mission through two simulation tests.The experiment results show that the improved A*-based algorithm reduces planning time by 30.05% and generates smoother paths than the classic A^(*) algorithm.The multi-objective arc-based method improves the rover’s motion efficiency,ensuring safer and quicker mission completion along the global path.
基金supported in part by the Research Grants Council of Hong Kong SAR(Grant No.14209020)and in part by the Peng Cheng Laboratory.
文摘Challenges in motion planning for multiple quadrotors in complex environments lie in overall°ight e±ciency and the avoidance of obstacles,deadlock,and collisions among themselves.In this paper,we present a gradient-free trajectory generation method for multiple quadrotors in dynamic obstacle-dense environments with the consideration of time consumption.A model predictive control(MPC)-based approach for each quadrotor is proposed to achieve distributed and asynchronous cooperative motion planning.First,the motion primitives of each quadrotor are formulated as the boundary state constrained primitives(BSCPs)which are constructed with jerk limited trajectory(JLT)generation method,a boundary value problem(BVP)solver,to obtain time-optimal trajectories.They are then approximated with a neural network(NN),pre-trained using this solver to reduce the computational burden.The NN is used for fast evaluation with the guidance of a navigation function during optimization to guarantee°ight safety without deadlock.Finally,the reference trajectories are generated using the same BVP solver.Our simulation and experimental results demonstrate the superior performance of the proposed method.
