Measurement of the internal stress and strain distributions within soft materials is necessary in the field of skin contact safety. However, conventional interactive force sensors cannot efficiently obtain or estimate these distributions. Herein, a shear strain sensor system consisting of distributed built-in piezoelectric polyvinylidene fluoride (PVDF) polymer films was developed to measure the internal shear strain field of a soft material. A shear strain sensing model was mathematically established, based on the piezoelectricity and mechanical behavior of a bending cantilever beam, to explain the sensing principle. An experiment in three-dimensional measurement of the shear strain distribution within an artificial skin was designed and conducted to assess the sensitivity of the sensing model. This sensor system could visualize the shear strain field and was sensitive to different contact conditions. The measurement results agreed well with the results of numerical simulation of the substrate, based on contact mechanics. The proposed sensor system was confirmed to provide a new sensing method for the field of shape analysis. The sensor system can be applied to develop sufficiently sensitive electronic skin and can significantly contribute to skin damage analysis and skin contact safety assessment.

Soft robotics require flexible sensors that can realize sensitive measurements for which rigid sensors are not viable. Precise and quantitative measurement of the mechanical phenomena is necessary in all the sensors; however, in the field of soft robotics, the complex structure and physical properties of composite materials complicate the construction of a precise sensing model. In this study, a built-in sensor system for internal shear strain and stress distribution measurement is developed by embedding piezoelectric polyvinylidene fluoride polymer films within the objective soft material. Shear-strain sensing models are established by relating the piezoelectricity and cantilever bending mechanics. For validation, experiments are conducted in which the deflection of the embedded film sensors is monitored and digitized using a video camera. The shear-strain sensing model is validated by evaluating the agreement between the modeled and measured physical variations, and a high coefficient of determination of 0.974 is obtained. In addition, based on the linear relation of the arithmetic expression of each sensing model, calibrated results for internal shear strain and stress sensing are successfully obtained. Furthermore, the measurement effect of this type of flexible sensor and its potential applications are discussed.

This paper presents a fully onboard control and planning system for indoor quadrotor navigation, leveraging modular control and lightweight motion planning. Autonomous operation of unmanned aerial vehicles (UAVs) in indoor environments, such as inspection, monitoring, and mapping, faces challenges due to the absence of global navigation satellite system (GNSS) and limited onboard computational resources. Existing methods often rely on external localization systems or high-specification CPUs, restricting deployment on compact, low-power UAVs. To address this, we develop a hierarchical control architecture comprising motor, attitude, velocity, and position controllers, all implemented with full-state feedback. Paired with a custom lightweight motion planning algorithm, the system operates efficiently on resource-constrained CPUs such as the Raspberry Pi. This approach enables affordable, low-power UAVs to achieve autonomous indoor navigation without external infrastructure. The results show stable flight performance and computational efficiency, validating suitability for GNSS-denied environments.

The teleoperation feature of a leader–follower robot is used to operate the robot in a working environment that is dangerous to the operator. This paper proposes a method for estimating the intended operation of the operator of the leader–follower robot and generating behaviors for the follower robot. By generating partially autonomous robot behavior, our proposed method decreases the burden on the operator and realizes a safe controller that is robust against misoperation or disturbances. Owing to the impact of misoperation on estimation and the potential for unintended movement of the follower robot, our proposed estimation method relies on historical values of intended operation. The proposed method was verified through simulation using real operation inputs to the leader system based on practical scenarios, including misoperation.

Leader–follower robots employ teleoperation, enabling flexible and safe operation. A robotic surgical system, as a typical leader–follower system, enables remote manipulation in minimally invasive surgery. However, inappropriate operator inputs may be directly transmitted to the patient side, potentially compromising safety. In this study, we propose a framework for estimating the operator’s operating strategy and suppressing motions caused by inappropriate operator inputs. The framework represents operating strategies on a strategy map defined by the degrees of precision, non-precision, and risk. A simplified fuzzy inference is used to estimate the degrees of precision and non-precision based on manipulation force and hand velocity, while the degree of risk is evaluated using gaze detection combined with the force and pressure applied by the instrument. Using processed kinematic and video data from the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), we validated the framework across gesture scenarios, including simulated cases of inappropriate operation. The results suggest that the proposed framework demonstrates the theoretical feasibility of effectively mitigating potentially unsafe motions within the simulated environment while preserving essential operational flexibility.