We address a path planning problem for heterogeneous multi-robot systems under specifications consisting of temporal constraints and routing tasks such as package delivery services. The robots are partitioned into several groups based on their dynamics and specifications. We introduce a concise description of such tasks, called a work, and extend counting LTL to represent such specifications. We convert the problem into an ILP problem. We show that the number of variables in the ILP problem is fewer than that of the existing method using cLTL+. By simulation, we show that the computation time of the proposed method is faster than that of the existing method.

In this study, a Coanda-drone with length, width, and height of 121.6, 121.6, and 191[mm] was designed, and its total mass was 1166.7[g]. Using four propulsion devices, it could produce a maximum of 5428[g] thrust. Its structure is very different from conventional drones because in this study it combines the design of the jet engine of a jet fixed-wing drone with the fuselage structure layout of a rotary-wing drone. The advantage of jet drone's high propulsion is kept so that it can output greater thrust under the same variation of PWM waveform output. In this study, the propulsion device performs high-speed jetting, and the airflow around the propulsion device will also be jetted downward along the direction of the airflow.

We have developed an all-optical fiber link antenna measurement system for a millimeter wave 5th generation mobile communication frequency band around 28 GHz. Our developed system consists of an optical fiber link an electrical signal transmission system, an antenna-coupled-electrode electric-field (EO) sensor system for 28GHz-band as an electrical signal receiving system, and a 6-axis vertically articulated robot with an arm length of 1m. Our developed optical fiber link electrical signal transmission system can transmit the electrical signal of more than 40GHz with more than -30dBm output level. Our developed EO sensor can receive the electrical signal from 27GHz to 30GHz. In addition, we have estimated a far field antenna factor of the EO sensor system for the 28GHz-band using an amplitude center modified antenna factor estimation equation. The estimated far field antenna factor of the sensor system is 83.2dB/m at 28GHz.

This paper proposes a fault-resilient ROS platform supporting rapid fault detection and recovery. The platform employs heartbeat-based fault detection and node replication-based recovery. Our prototype implementation on top of the ROS Melodic shows a great performance in evaluations with a Nvidia development board and an inverted pendulum device.

Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.

Robot motion planning is an important part of the unmanned supermarket. The challenges of motion planning in supermarkets lie in the diversity of the supermarket environment, the complexity of obstacle movement, the vastness of the search space. This paper proposes an adaptive Search and Path planning method based on the Semantic information and Deep reinforcement learning (SPSD), which effectively improves the autonomous decision-making ability of supermarket robots. Firstly, based on the backbone of deep reinforcement learning (DRL), supermarket robots process real-time information from multi-modality sensors to realize high-speed and collision-free motion planning. Meanwhile, in order to solve the problem caused by the uncertainty of the reward in the deep reinforcement learning, common spatial semantic relationships between landmarks and target objects are exploited to define reward function. Finally, dynamics randomization is introduced to improve the generalization performance of the algorithm in the training. The experimental results show that the SPSD algorithm is excellent in the three indicators of generalization performance, training time and path planning length. Compared with other methods, the training time of SPSD is reduced by 27.42% at most, the path planning length is reduced by 21.08% at most, and the trained network of SPSD can be applied to unfamiliar scenes safely and efficiently. The results are motivating enough to consider the application of the proposed method in practical scenes. We have uploaded the video of the results of the experiment to https://www.youtube.com/watch?v=h1wLpm42NZk.

The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti-gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.

While the introduction of softwarelization technologies such as software-defined networking and network function virtualization transfers the main focus of network management from hardware to software, network operators still have to deal with various and numerous network and computing equipment located in network centers. Toward fully automated network management, we believe that a robotic approach will be essential, meaning that physical robots will handle network-facility management works on behalf of humans. This paper focuses on robotic assistance for on-site network maintenance works. Currently, for many network operators, some network maintenance works (e.g., hardware check, hardware installation/replacement, high-impact update of software, etc.) are outsourced to computing and network vendors. Attendance (witness work) at the on-site vendor's works is one of the major tasks of network operators. Network operators confirm the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three essential works of robots, namely delegated attendance at on-site meetings, progress check by periodical patrol, and remote monitoring, which support the various forms of attendance. The paper presents our implementation of enabling these forms of support, and reports the results of experiments conducted in a commercial network center.

Self-review is essential to improving presentation, particularly for novice/unskilled researchers. In general, they could record a video of their presentation, and then check it out for self-review. However, they would be quite uncomfortable due to their appearance and voice in the video. They also struggle with in-depth self-review. To address these issues, we designed a presentation avatar that reproduces presentation made by researchers. The presentation avatar intends to increase self-awareness through self-reviewing. We also designed a checklist to aid in a detailed self-review, which includes points to be reviewed. This paper also demonstrates presentation avatar systems that use a virtual character and a robot, to allow novice/unskilled researchers as learners to self-review their own presentation using the checklist. The results of case studies with the systems indicate that the presentation avatar systems have the potential to promote self-review. In particular, we found that robot avatar promoted engagement in self-reviewing presentation.

Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.

In this study, we generate dialogue contents in which two systems discuss their distress with each other. The user inputs sentences that include environment and feelings of distress. The system generates the dialogue content from the input. In this study, we created dialogue data about distress in order to generate them using deep learning. The generative model fine-tunes the GPT of the pre-trained model using the TransferTransfo method. The contribution of this study is the creation of a conversational dataset using publicly available data. This study used EmpatheticDialogues, an existing empathetic dialogue dataset, and Reddit r/offmychest, a public data set of distress. The models fine-tuned with each data were evaluated both automatically (such as by the BLEU and ROUGE scores) and manually (such as by relevance and empathy) by human assessors.

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.

Heterogeneous drone swarms are large hybrid drone clusters in which multiple drones with different wireless protocols are interconnected by some translator drones called GWs. Nowadays, because inexpensive drones, such as toy drones, have become widely used in society, the technology for constructing huge drone swarms is attracting more and more attention. In this paper, we propose an autonomous GW mobility control algorithm for establishing stabilized and low-delay communication among heterogeneous clusters, assuming that only GWs are controllable and relocatable to ensure the flexible operationality of drone swarms. Our proposed algorithm is composed of two independent sub algorithms - the Link Stabilizer and the Path Optimizer. The Stabilizer maintains the neighbor links and consists of two schemes: the neighbor clustering based on relative velocities and the GW velocity calculation using a kinetic model. The Optimizer creates a shortcut to reduce the end-to-end delay for newly established communication by relocating the GW dynamically. We also propose a conceptual protocol design to implement this algorithm into real-world drone swarms in a distributed manner. Computer simulation reveals that the Stabilizer improved the connection stability for all three mobility models even under the high node mobility, and the Optimizer reduced the communication delay by the optimal shortcut formation under any conditions of the experiments and its performance is comparable to the performance upper limit obtained by the brute-force searching.

In this paper, we demonstrate that a formal approach is effective for improving reliability of cooperative robot designs, where the control logics are expressed in concurrent FSMs (Finite State Machines), especially in accordance with the standard FSM4RTC (FSM for Robotic Technology Components), by a case study of cooperative transport robots. In the case study, FSMs are modeled in the formal specification language CSP (Communicating Sequential Processes) and checked by the model-checking tool FDR, where we show techniques for modeling and verification of cooperative robots implemented with the help of the RTM (Robotic Technology Middleware).

Autonomous mobility machines, such as self-driving cars, transportation robots, and automated construction machines, are promising to support or enrich human lives. To further improve such machines, they will be connected to the network via wireless links to be managed, monitored, or remotely operated. The autonomous mobility machines must have self-status based on their positioning system to safely conduct their operations without colliding with other objects. The self-status is not only essential for machine operation but also it is valuable for wireless link quality management. This paper presents self-status-based wireless link quality prediction and evaluates its performance by using a prototype mobility robot combined with a wireless LAN system. The developed robot has functions to measure the throughput and receive signal strength indication and obtain self-status details such as location, direction, and odometry data. Prediction performance is evaluated in offline processing by using the dataset gathered in an indoor experiment. The experiments clarified that, in the 5.6 GHz band, link quality prediction using self-status of the robot forecasted the throughput several seconds into the future, and the prediction accuracies were investigated as dependent on time window size of the target throughput, bandwidth, and frequency gap.

This paper reviews our developed wide band human body communication technology for wearable and implantable robot control. The wearable and implantable robots are assumed to be controlled by myoelectric signals and operate according to the operator's will. The signal transmission for wearable robot control was shown to be mainly realized by electrostatic coupling, and the signal transmission for implantable robot control was shown to be mainly determined by the lossy frequency-dependent dielectric properties of human body. Based on these basic observations on signal transmission mechanisms, we developed a 10-50MHz band impulse radio transceiver based on human body communication technology, and applied it for wireless control of a robotic hand using myoelectric signals in the first time. In addition, we also examined its applicability to implantable robot control, and evaluated the communication performance of implant signal transmission using a living swine. These experimental results showed that the proposed technology is well suited for detection and transmission of biological signals for wearable and implantable robot control.

When considering the deployment of a radio communications network, the study of multipath interference and its impact on the quality of signal reception is of the outmost importance in order to meet the necessary performance requirements. This work considers specifically the case of the lunar surface as the mission scenario for a community of autonomous mobile exploration robots, which communicate through a radiofrequency network to accomplish their mission. In this application, the low height of the mobile robots makes the influence of multipath interference effects on the performance of the radio communication channel relevant. However, no specific information about lunar soil reflection coefficients characteristics is available for radiofrequency communication bands. This work reviews the literature on the electrical parameter of Lunar soil. From this base, the reflection coefficients are estimated for the assumed radio profile in different communications frequency bands. Finally, the results obtained are discussed.

In order to realize intelligent robot system, it is required to process large amount of data input from complex and different kinds of sensors in a short time. FPGA is expected to improve process performance of robots due to better performance per power consumption than high performance CPU, but it has lower development productivity than software. In this paper, we discuss automatic generation of FPGA components for robots. A design tool, developed for easy integration of FPGA into robots, is proposed. The tool named cReComp can automatically convert circuit written in Verilog HDL into a software component compliant to a robot software framework ROS (Robot Operation System), which is the standard in robot development. To evaluate its productivity, we conducted a subject experiment. As a result, we confirmed that the automatic generation is effective to ease the development of FPGA components for robots.

In this paper, we propose a new asymptotically stabilizing control law for a four-wheeled vehicle with a steering limitation. We adopt a locally semiconcave control Lyapunov function (LS-CLF) for the system. To overcome the nonconvexity of the input-constraint set, we utilize a saturation function and a signum function in the control law. The signum function makes the vehicle velocity nonzero except at the origin so that the angular velocity can be manipulated within the input constraint. However, the signum function may cause a chattering phenomenon at certain points of the state far from the origin. Thus, we integrate a lazy-switching mechanism for the vehicle velocity into the control law. The mechanism makes a sign of the vehicle velocity maintain, and the new control input also decreases the value of the LS-CLF. We confirm the effectiveness of our method by a computer simulation and experiments.

Herein, we proposed a robot model that will obey a norm of a certain group by interacting with the group members. Using this model, a robot system learns the norm of the group as a group member itself. The people with individual differences form a group and a characteristic norm that reflects the group members' personalities. When robots join a group that includes humans, the robots need to obey a characteristic norm: a group norm. We investigated whether the robot system generates a decision-making criterion to obey group norms by learning from interactions through reinforcement learning. In this experiment, human group members and the robot system answer same easy quizzes that could have several vague answers. When the group members answered differently from one another at first, we investigated whether the group members answered the quizzes while considering the group norm. To avoid bias toward the system's answers, one of the participants in a group only obeys the system, whereas the other participants are unaware of the system. Our experiments revealed that the group comprising the participants and the robot system forms group norms. The proposed model enables a social robot to make decisions socially in order to adjust their behaviors to common sense not only in a large human society but also in partial human groups, e.g., local communities. Therefore, we presumed that these robots can join human groups by interacting with its members. To adapt to these groups, these robots adjust their own behaviors. However, further studies are required to reveal whether the robots' answers affect people and whether the participants can form a group norm based on a robot's answer even in a situation wherein the participants recognize that they are interacting in a group that include a real robot. Moreover, some participants in a group do not know that the other participant only obeys the system's decisions and pretends to answer questions to prevent biased answers.