As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer...As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.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
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
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
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
基金Project supported by the National Natural Science Foundation of China(No.U21A20120)。
文摘As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.
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.
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.
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.
