In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence