摘要
Most existing force feedback methods are still difficult to meet the requirements of real-time force calculation in virtual assembly and operation with complex objects. In addition, there is often an assumption that the controlled objects are completely flee and the target object is only completely fixed or flee, thus, the dynamics of the kinematic chain where the controlled objects are located are neglected during the physical simulation of the product manipulation with force feedback interaction. This paper proposes a physical simulation method of product assembly and operation manipulation based on statistically learned contact force prediction model and the coupling of force feedback and dynamics. In the proposed method, based on hidden Markov model （HMM） and local weighting learning （LWL）, contact force prediction model is constructed, which can estimate the contact force in real time during interaction. Based on computational load balance model, the computing resources are dynamically assigned and the dynamics integral step is optimized. In addition, smoothing process is performed to the force feedback on the synchronization points. Consequently, we can solve the coupling and synchronization problems of high-frequency feedback force servo. low-frequency dynamics solver servo and scene rendering servo, and realize highly stable and accurate force feedback in the physical simulation of product assembly and operation manipulation. This research proposes a physical simulation method of product assembly and operation manipulation.
Most existing force feedback methods are still difficult to meet the requirements of real-time force calculation in virtual assembly and operation with complex objects. In addition, there is often an assumption that the controlled objects are completely flee and the target object is only completely fixed or flee, thus, the dynamics of the kinematic chain where the controlled objects are located are neglected during the physical simulation of the product manipulation with force feedback interaction. This paper proposes a physical simulation method of product assembly and operation manipulation based on statistically learned contact force prediction model and the coupling of force feedback and dynamics. In the proposed method, based on hidden Markov model （HMM） and local weighting learning （LWL）, contact force prediction model is constructed, which can estimate the contact force in real time during interaction. Based on computational load balance model, the computing resources are dynamically assigned and the dynamics integral step is optimized. In addition, smoothing process is performed to the force feedback on the synchronization points. Consequently, we can solve the coupling and synchronization problems of high-frequency feedback force servo. low-frequency dynamics solver servo and scene rendering servo, and realize highly stable and accurate force feedback in the physical simulation of product assembly and operation manipulation. This research proposes a physical simulation method of product assembly and operation manipulation.
基金
Supported by National Natural Science Foundation of China(51475418)
National Basic Research 973 Program of China(2011CB706503)
Science Fund for Creative Research Groups of National Natural Science Foundation of China(51221004)
