A Fast Filtering Algorithm for Continuous Constraint Satisfaction Problems

We propose an efficient filtering algorithm to solve for continuous constraint satisfaction systems. The algorithm uses an approximate method, named Landmark-based Approximate Inference (LAI) to find landmark-based values of variables in constraint system. The method used the landmark-based values for finding not only local consistency but also global consistency of continuous constraint system. The key idea of this approach is to generate candidates of landmark-based values to be utilized during the constraint propagation to facilitate a quiescent condition. This approach solved separate subintervals in solutions problem to get global consistency, handles complicated problems with a low cost and captures the solutions on various types of constraints. The algorithm is also compared with other methods in real-word applications.