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Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Identification of the direction of the sound source is very important for human–machine interfacing in the applications such as target detection on military applications and wildlife conservation. Considering its vast applications, this study aims to design, simulate, fabricate and test a bidirectional acoustic sensor having two cantilever structures coated with piezoresistive material for sensing has been designed, simulated, fabricated and tested.

The structure is a piezoresistive acoustic pressure sensor, which consists of two Kapton diaphragms with four piezoresistors arranged in Wheatstone bridge arrangement. The applied acoustic pressure causes diaphragm deflection and stress in diaphragm hinge, which is sensed by the piezoresistors positioned on the diaphragm. The piezoresistive material such as carbon or graphene is deposited at maximum stress area. Furthermore, the Wheatstone bridge arrangement has been formed to sense the change in resistance resulting into imbalanced bridge and two cantilever structures add directional properties to the acoustic sensor. The structure is designed, fabricated and tested and the dimensions of the structure are chosen to enable ease of fabrication without clean room facilities. This structure is tested with static and dynamic calibration for variation in resistance leading to bridge output voltage variation and directional properties.

This paper provides the experimental results that indicate sensor output variation in terms of a Wheatstone bridge output voltage from 0.45 V to 1.618 V for a variation in pressure from 0.59 mbar to 100 mbar. The device is also tested for directionality using vibration source and was found to respond as per the design.

The fabricated devices could not be tested for practical acoustic sources due to lack of facilities. They have been tested for a vibration source in place of acoustic source.

The piezoresistive bidirectional sensor can be used for detection of direction of the sound source.

In defense applications, it is important to detect the direction of the acoustic signal. This sensor is suited for such applications.

The present paper discusses a novel yet simple design of a cantilever beam-based bidirectional acoustic pressure sensor. This sensor fabrication does not require sophisticated cleanroom for fabrication and characterization facility for testing. The fabricated device has good repeatability and is able to detect the direction of the acoustic source in external environment.

This paper aims to present numerical simulations for a series of flight processes for the postlaunching stage of the “balloon-borne UAV system.” It includes the balloon further ascent motion after airborne launching. In terms of unmanned aerial vehicles (UAVs), the tailspin state and the charge-out process with an anti-tailspin parachute-assisted suspending are analyzed. Then, the authors conduct trajectory optimization simulations for the long-distance gliding process.

The balloon kinematics model and the parachute Kane multibody dynamic model are established. Using steady-state tailspin to reduced-order analysis and achieving change-out simulation by parachute suspension dynamic model. A reentry optimization control problem is developed and the Radau pseudo-spectral method is used to calculate the glide trajectory.

The established dynamic model and trajectory optimization method can effectively simulate the motion process of balloons and UAVs. The system mass reduction for launching UAVs will not cause damage to the balloon structure. The anti-tailspin parachute can reduce the UAV attack angles effectively. The UAV can glide to the designated target position by adjusting the attack angle and sideslip angle. The farthest flight distance after launching from 20 km height is 94 km and the gliding time is 40 min, which demonstrates the potential application advantage of high-altitude launching.

The research content and related conclusions of this article achieve a closed-loop analysis of the flight mission chain for the “balloon-borne UAV system,” which provides simulation references for relevant balloon launching experiments.

This paper establishes a complete set of numerical simulation models and can effectively analyze various postlaunching behaviors.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.