Polynomial joint angle arm robot motion planning in complex geometrical obstacles

Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter

“…4 shows that the hypermutation GA-KS method gives better performance than the low mutation GA-KS method. Superiority of the hyper-mutation GA-KS method over the low mutation GA-KS method is also reported in the previous research [21,22].…”

Zero Distribution of System with Unknown Random Variables Case Study: Avoiding Collision Path

“…In case of collision-free, the pattern of avoiding collision polynomial can be model as proposed approach. This paper uses the computational approach in [9,10], as benchmark case to predict the randomness of avoiding collision polynomial using the proposed method. …”

“…The proposed method will be applied to predict the pattern of k ã for 3-DOF planar robot in [9] and 5-DOF planar robot in very crowded obstacles. For 3-DOF planar robot, the proposed method has succeeded to predict the collision-free polynomial coefficient.…”

“…The collision-free polynomial coefficients need to be searched. This paper will use the polynomial degree sixth as avoiding collision joint angle path as used in [9].…”

“…It means the polynomial coefficient matrix is composed from proper values of the real number. Machmudah et al [9] use Genetic algorithm (GA) and Particle Swarm Optimization (PSO) in finding the feasible coefficient polynomial. Although the GA and PSO can be used to find this feasible coefficient and show good performance in evolving it; however, the pattern of avoiding collision polynomial coefficient is still unknown, except just random.…”