Multi-objective Solving Method for Feedback Motion Controller
Zhang Yingkai,Xie Wenjun,Liu Xiaoping(School of Computer and Information, Hefei University of Technology, Hefei 230601)
Objective The physical-based animation synthesis can generate the human physical motion which satisfies physical laws. Human physical motion is generated by responding to the environment in real-time, and is not mechanically repetitive. Therefore, human physical motion has been one of the hottest topics in the fields of computer graphics and virtual reality in recent years. However, human physical motion is very difficult to generate due to high dimensionality, nonlinearity and strong coupling of joints in human physical model. In addition, especially in the diverse environment or under external forces, feedback controller is often used for controlling human physical motion. During the process of solving the feedback controllers, multiple objective functions are usually designed. Researchers apply optimization methods to solve feedback controller. Multiple objective functions are often converted to single objective function by utilizing the weighted sum method. However, improper weights can easily result in a failure of convergence due to the local traps, the setting of weights is therefore crucial to the direction of optimization, the convergence time and the result. This makes experiment heavy and so often these systems require dedicated technical developers to maintain. In view of the above problem, this paper proposes a multi-objective solving method for feedback motion controller.Method Firstly, we preprocesses motion data and extracts key frames to construct the initial controller. Meanwhile, an improved feedback control mechanism is designed to reduce the difficulty of constraint-solving problem. The parent individuals of population generate children individuals after variation. Nevertheless, there are lots of failure individuals which do not satisfy constraints in the children population. To solve this problem, we utilizes forbidden region pre-filtering strategy which adopts the support vector machine (SVM) with radial basis kernel function (RBF) to remove these failure individuals. In order to supplement these removed individuals, we replenish new children individuals through re-sampling; For the sake of measuring quality of every individual, we set several objective functions, including root cost function, pose cost function, and energy cost function. Then, every individual controller is inputted to the physical simulation system, and multi-objective fitness values are obtained after simulations are finished. In order to select the excellent individuals which show uniform distribution to construct the next generation of parent individuals, the regional density multi-layer optimization algorithm is adopted. At the same time, the SVM with kernel RBF is updated. In order to get more stable controllers which can generate longer physical motion, we prune some individual controllers which merely generate short physical motion. After that, we decides whether to enter the next stage of optimization through multi-stage physical solving algorithm based on pruning. After many iterations, the optimized physical controller is obtained. Finally the optimal controller is applied to generate human physical motion with feedback.Result Based on the proposed method, the experiments are conducted on multiple test functions and physical motions. In the test function experiment, the classical test functions are used for experimental comparison in this paper, including LZ1 function, ZDT2 function, and DTLZ4 function, and we also set several user-defined constraints respectively in order to analyze the methods’ anti-constraints ability. Comparing to the existing methods, the proposed method achieves higher hit ratio of excellent individuals which satisfy user-defined constraints in the children population under the same number of iterations. The final optimal solution set achieves smaller inverted generational distance (IGD) and shows more uniform distribution. In the physical motion experiment, the generation of human physical motion is carried out for walking, running and rolling respectively. Several constraints are appointed, for example, the human physical model is forbidden from falling down and ricocheting off while in the simulation of locomotion. Comparing to the existing methods, the proposed method achieves higher convergence speed and obtains smaller objective function values. The results indicate our generated physical motion has better performance than the others.Conclusion The proposed method can generate a variety of physical human motion controllers, and these controllers not only can generate physical motion, but also can maintain physical model’s balance under external forces. The proposed evolutionary method can solve the problems that it is difficult to set multi-objective weights and conduct long-time optimization; In addition, the proposed method also has a good performance in terms of other multi-objective problems with constraints.