Earticle

From a design perspective, there are two ways in robot design approach: biologically inspired and functionally designed. It has been proved that the two robot design types could be perceived differently by the cultural background. This study explored the impact of robot design types in Korean and Japanese culture on product evaluation, product usefulness, and purchase intention. We conducted an experiment comparing a group of functionally designed robots having single functions each and a biologically inspired robot having multifarious functions itself. The results showed that Korean participants evaluated the functionally designed robot more positively than the biologically inspired robot on product evaluation and product usefulness whereas the evaluation was more positive toward the biologically inspired robot than the functionally designed robot for Japanese participants. While Korean participants were willing to purchase the functionally designed robot as they perceived it positively and useful than the biologically inspired robot, Japanese participants showed no significant difference on purchase intention by robot design types. These results suggested that robot designers should consider not only the robot design types but also the cultural differences for making better customer acceptance. The implication for the effective robot design approach is discussed according to the cultural differences.

In Ant Colony System, the agents or the ants travel in search of foods by following one another that is they show the swarming behavior along the searching path of the food source and return path towards the hive. In the artificial ant system or the robot system, the same operation of the robot can be observed by the suitable design of the travelling path. The path design and the travelling technique are already proposed in my two previous works. Those designs include different types of algorithms for different opeartions. Kruskel’s algorithm is applied to find the shortest path in between any two nodes, one is the source and another is the destination node. But the disadvantage of this design is the longer simulation time as three algorithms is required to travel from one node to another node and naturally several numbers of the databases are required to hold all the information about the entire robot colony. In this paper, a single database is proposed that can hold all the connectivity and which can be driven with a single algorithm. The proposed method is compared with the other previous techniques of finding the shortest path and the result set represented here showing the efficiency of the proposed method.

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

This paper considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. The Paradigm for neurocomputing that has its roots in rough set theory, and works well in cases where there is uncertainty about the values of measurements used to make decisions. In the case of the line-crawling robot (LCR) described in this paper, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons...