We propose a new platooning vehicle that uses a coupling device not for the traction of following vehicles but for the measurement of relative position and orientation between leading and following vehicles. The following vehicles are driven by their own motors. This paper reports development of the relative position and orientation measurement system. In the coupling device, multiple distance sensors are equipped around the pin. These sensors acquire the distance between the pin and a hexagonal ring. The array of distance values is converted to the relative position and orientation of vehicles using two methods: (1) Geometric method and (2) Data look-up method. After a prototype of the proposed measurement system is implemented, it is evaluated using hardware in the loop simulation (HILS) that simulates multiple vehicles' platooning drive. Therefore, the system capabilities have been evaluated not only by the measurement precision but also by the platooning performance. The experimentally obtained results demonstrate that the proposed measurement system is sufficiently valid and feasible. The data look-up method exhibits especially superior performance because it uses no geometric conditions explicitly.

In this paper, aiming to support easy walking route planning, we propose methods for predicting a heart rate along the arbitrary route without walking data, and recommending a semi-optimal walking route based on the predicted results. In our method, we build a model to predict the heart rate during walking with expected walking speed and gradient along a target route, and compute a semi-optimal walking route (near least physical load route satisfying calorie/distance constraints requested by a user) by using the model. In order to evaluate the accuracy of the prediction model, a walking experiment with 39 participants was conducted. The result showed that our model could predict the heart rate with mean absolute error (MAE) of 6.31 beats per minute on average. We also confirmed that the route recommended by our method satisfied calorie/distance constraints requested by a user while keeping the average and the maximum physical load (in terms of heart rate reserve) at 29.5% (light load) and 44.4% (moderate load), respectively.

Printable electronic circuits have received a big adoption from a variety of users such as researchers, hobbyists, designers, and children. The designers want to use electronic circuits along with graphic design yet focus on the creativity and aesthetics of the design. However, current technology requires them to take care of the discouraging electrical behaviors of the circuits. Taking the task of lighting up a bunch of light-emmited diodes (LEDs) as an example, it sounds simple, but is posing significant challenges for inexperience users. Given the non-negligible resistance of conductive ink, it is not straightforward to generate a pattern that lights up the LEDs evenly. Furthermore, a large number of LEDs make it difficult and error-prone to wire them efficiently. It is possible to try existing auto-routers in computer aided design tools to automatically route these LEDs. However, being optimized to make circuits with highly conductive materials such as copper and gold, these auto-routers ignore the intrinsic resistance of the conductive ink. In this paper, we propose an LED auto-router which computationally generates a conductive pattern to balance brightness of multiple LEDs without the need of additional resistors. Our routing algorithm is based on the traveling salesman problem to find the shortest cross-less path through the LEDs, and thus minimize the ink consumption. It then adjusts resistances of the conductive patterns to regulate the current which flows through each LED.

Collaboration between computer graphics and multiple robots has attracted increasing attention in several fields. To enhance the seamless connection between them, the system should be able to accurately determine the position and state of the robots and to control them easily and instantly. However, realizing a responsive control system for a large number of mobile robots without complicated settings while avoiding the system load problem is not trivial. We propose a novel system, called “Phygital Field,” for the localization and control of multiple mobile robots. Utilizing pixel-level visible light communication technology, our system can project two types of information in the same location: visible images for humans and data patterns for mobile robots. The system uses coded light superimposed onto a visual image and projected onto the robots. The robots localize their position by receiving and decoding the projected light and can follow a target using the coded velocity vector field. Localization and control information can be independently conveyed in each pixel, and we can change this information over time. The system only requires a projector to control the robot swarm; thus, it can be used on any projection surface. We experimentally assess the localization accuracy of our system for both stationary and moving robots. To further illustrate the utility of our proposed system, we demonstrate the control of multiple mobile robots in spatially and temporally varying vector fields. We also propose prototype applications that can provide users with novel content from collaboration between computer graphics and robot swarm.

Despite their high energy consumption, office thermal comfort delivery mechanisms perform poorly. The recently enacted environmental protection policies, which require a significant cutback in greenhouse gas emission, can only exacerbate this situation because, given the limitations of current thermal comfort provision technologies, a reduction in energy would translate into an increased thermal discomfort in offices. Hence, this dilemma entails alternative thermal comfort delivery systems that provide higher quality thermal comfort at lower energy. This paper proposes to use physiologically-controlled thermal comfort controllers to achieve this. It also discusses advantages of this novel approach, highlights potential unobtrusive thermal comfort biomarkers, and presents the necessary steps in designing such systems. Finally, the paper briefly discusses some of our preliminary results that showcase the feasibility of such a system.

This paper introduces a reinforcement learning technique with an internal reward for a multi-agent cooperation task. The proposed methods is an extension of Q-learning which changes the ordinary (external) reward to the internal reward for agent-cooperation. Specifically, we propose here two Q-learning methods, both of which employ the internal reward for the less or no communication. To guarantee the effectiveness of the proposed methods, we theoretically derived the mechanisms that solve the following questions: (1) how the internal rewards should be set to guarantee the cooperation among the agents under the condition of less and no communication; and (2) how the values of the cooperative behaviors types (i.e., the varieties of the cooperative behaviors of the agents) should be updated under the condition of no communication. The intensive simulations on the maze problem for the agent-cooperation task have been revealed that our two proposed methods successfully enable the agents to acquire their cooperative behaviors even in less or no communication, while the conventional method (Q-learning) always fails to acquire such behaviors.

This paper proposes a weighted opinion-sharing method called conformity-autonomous adaptive tuning (C-AAT) that enables agents to communicate and share correct information in a small-world network even when the links and information change dynamically. Concretely, each agent estimates weights for each of its neighbors by comparing their opinions with its own, increasing the weight if both are the same and decreasing it otherwise. To investigate the proposed method's effectiveness, experiments were conducted for three scenarios: (1) a static network with sensor agents that were almost equally likely to share incorrect environment information; (2) a static network with sensor agents whose probability of sharing incorrect information changed over time; (3) a dynamic network where some agent links were randomly cut over time. The experimental results led to three conclusions about C-AAT: (i) it can make the agents' opinions robust against incorrect sensor agent opinions by decreasing the weights; (ii) it can decrease the weights of agents conveying incorrect opinions with varying probabilities to prevent incorrect opinions being shared; and (iii) it can help agents share correct opinions by increasing the weights of their neighbors even if the agents receive fewer opinions due to links being cut.

Force measurement using zero-compliance mechanism is proposed and studied experimentally. A zero-compliance mechanism is composed of two suspensions connected in series; one of them is operated to cancel the deflection of the other so that the total length of the suspension is maintained even if force acts between the ends. In the proposed measurement system, one of the ends becomes the point of force and the point of the connection becomes the detection point. The force is estimated from the displacement of the detection point. An apparatus is fabricated for experimental study to show the validity of the proposed measurement method. Double series magnetic suspension is used to achieve zero compliance. In the apparatus, the motions of two suspended objects (floators) are controlled with a single electromagnet. Each floator is supported by leaf springs to move in the vertical direction translationally. The attractive force of the electromagnet directly acts on the first floator in which a permanent magnet is installed. The motion of the second floator is controlled indirectly through the attractive force of the permanent magnet. To achieve the zero compliance characteristic, PID control is applied to the second floator. It is confirmed experimentally that the deflection of the second suspension, i.e., the distance between the first and second floators, is proportional to external force acting on the second floator. This result indicates that the force can be estimated for the deflection of the second suspension. It is also demonstrated that dynamic force can be measured by the proposed method.

In this paper, we deal with the particle swarm optimization (PSO) which is one of metaheuristic methods for global optimization. In this paper, we propose a PSO method using a chaotic system, where each particle keeps its best solution called the pbest, and it searches around multiple pbests of which function values are not more than that of one's own pbest. Since the proposed method updates the particle's position by using a perturbation-based chaotic system, each particle is not trapped at any local minimum solution. In addition, the method can be expected to search for solutions effectively by keeping a balance between the expansion of the searching area and the limitation of the area based on the qualities of the multiple pbests. Through numerical experiments, we verify the advantages of the proposed method.

This study demonstrates that various unknown parameters used in nonlinear models of McKibben pneumatic artificial muscles (PAMs) can characterize the features of McKibben PAM products. By focusing on a parameter space in the PAM model, this study employs a support vector machine to determine which unknown parameters characterize each PAM product. For validation, we analyze five different PAM products to observe whether the resulting minimal combination of parameters will help to identify the product. The observations of our analysis provide prior PAM knowledge that can be used to develop efficient parameter estimation and capture aging degradation, which are important for robust estimation and control in PAM systems.

This paper proposes a control scheme based on chance-constrained optimization for spark-ignition engines to ensure transient torque tracking performance with improving thermal efficiency under chance-constraint for combustion phase. Firstly, the optimal equilibrium operating points are obtained by solving a chance-constrained optimization problem offline based on scenario approach. Then, linear quadratic regulator is applied to control the engine operate at the optimal equilibrium operating points under certain torque demand and engine speed. The proposed method is experimentally validated on a commercial gasoline engine, and the results demonstrate the method's performance.

In industrial control system (ICS) network, communication is often conducted using custom protocols. Methods for analysis and protection from cyber threats that are specific to ICS network need to be discussed in line with each device and system specification. In this research, the honeypot technology, which is already practiced in IT networks, was further improved for ICS networks so that it responds to packets reaching the honeypots and even conducts counter-scan to collect information of the attack method and its sources. It has been already presented that machines infected with some known malware (e.g. Havex RAT) in ICS networks conduct scan activities against certain devices. For this type of attack, interaction honeypot is considered effective in identifying infected devices out of such scans. In the simulation based on Modbus Stager, which affects programmable logic controller (PLC) operation and connected PCs, the suggested interaction honeypot, namely “traceback honeypot system (THS)” successfully collected payload that is actually sent in the attacks by emulating responses to commands on Modbus protocols. Information obtained from THS-based observation can be used for proactive purposes as in separating infected devices from the operating network and restricting access to certain devices to prevent further infection in the ICS network. This paper discusses methods of tracking attack sources using the THS and preventing further infection within the network based on the search result.