Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm

Visual Perception for a Partner Robot Based on Computational Intelligent

We propose computational intelligence for partner robot perception in which the robot requires the capability of visual perception to interact with human beings. Basically, robots should conduct moving object extraction, clustering, and classification for visual perception used in interactions with human beings. We propose total human visual tracking by long-term memory, k-means, self-organizing map, and a fuzzy controller is used for movement output. Experimental results show that the partner robot can conduct the human visual tracking

Evolutionary Robot Vision for People Tracking Based on Local Clustering

This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and a human tracking method based on the evolutionary robot vision. Finally, we show experimental results of the human tracking to discuss the effectiveness of our proposed method

A Study of Interactive Genetic Algorithm for Human-Friendly Trajectory Generation of a Robot Arm

This work deals with human-friendly trajectory generation of a robot arm. Various methods for the trajectory generation have been proposed so far, but robots must deal with environments including human operators. In this situation, the robot should take a suitable action/motion to the individual operator. This work applies an interactive genetic algorithm for the trajectory generation using human evaluation. Basically human evaluation is very important for generating robotic behavior, but the detail of the human evaluation is not clear. Therefore, the robot must estimate human evaluation through the optimization process, and use a state-value function used often in reinforcement learning. Furthermore, the effectiveness of the proposed method through some experiments of the robot arm will be discussed