What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.

As human-robot collaboration increases tremendously in real-world applications, a fully autonomous and reliable mobile robot for the collaboration has been a central research topic and investigated extensively in a large number of studies. One of the most pressing issues in such topic is the collision-free navigation that has a moving goal and unknown obstacles under the unstructured environment. In this paper, a cognitive robotic system is proposed for the robot to navigate itself to the moving target person without obstacle collision. This cognitive robotic system consists of a cognitive agent, which is created based on Soar cognitive architecture to reason its current situation and make action decision for the robot to avoid obstacles and reach the target position, and a speed planning module, which is based on dynamic window approach to generate appropriate linear and angular velocities for driving the robot’s motors. For the implementation of the proposed system, we use a differential drive wheel robot equipped with two ultra-wideband sensors and a color depth camera as the experimental platform. Finally, to evaluate the performance of our system in actual operating conditions, we conduct experiments with a scenario that includes main tasks: avoiding consecutive unknown obstacles and turning at corner while the robot follows continuously human user along the corridor.

In the age of knowledge-based economies, open innovation has increasing importance. This study aimed to explore the architectural design approaches that can revitalize innovation activities in the era of knowledge-based economies. This paper investigated global case research campuses, manufacturing systems, and innovation districts where architectural design supports innovation activities. This study developed a research framework of architectural design for innovation and applied it in the selected case studies to generate insights. First, the research campuses selected as case studies included Panopticon, DGIST Education and Research Campuses, and Apple Park. Second, the open innovation of manufacturing system architecture was analyzed through the case studies of the Ford Motor Company, Toyota Motor Corporation, and Rolls-Royce Motor Cars. Third, this paper studied the clustered open innovation architectures of Macquarie Park, One North, and Strijp-S Innovation Districts. The findings revealed how tacit knowledge motivates open innovation through the design of manufacturing systems, research campuses, and innovation districts through real examples and mathematical or concept model building.

This study proposes a computational design method for determining a hybrid power system’s sizing and ratio values that combines the national electric, solar cell, and fuel cell power sources. The inequality constraints associated with the ranges of power storage exchange and the stored energy are reflected as penalty functions in the overall cost function to be minimized. Using the energy hub model and the actual data for the solar cell power and the load of the residential sector in one Korean city for one hundred days, we optimize the ratio of fuel cell energy and solar cell energy to 0.46:0.54 through our proposed approach. We achieve an average cost-reduction effect of 19.35% compared to the cases in which the fuel-cell energy ratio is set from 0.1 to 0.9 in 0.1 steps. To optimize the sizing and the ratio of fuel-cell energy in the hybrid power system, we propose the modified version of the univariate dynamic encoding algorithm for searches (uDEAS) as a novel optimization method. The proposed novel approaches can be applied directly to any place to optimize an energy hub system model comprising three power sources, i.e., solar power, fuel cell, and power utility.

This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.