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Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 4075

Special Issue Editor

Dr. Yimin Zhou

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: control theory and engineering; neural network and machine learning; robotics; image processing; fault diagnosis and tolerant control; smart grid; UAV; autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous advancement of medical technology, more and more medical operations and rehabilitation tasks are beginning to require advanced medical equipment capable of providing accurate, reliable, and efficient diagnostic and treatment methods. Current traditional medical and rehabilitation equipment cannot offer sufficiently accurate perception and feedback capabilities comparable to the human tactile system. Bionic tactile systems, an emerging development, can be applied to surgical robots, intelligent prosthetics, and rehabilitation devices, offering significant application potential.

A deeper understanding of the impact mechanisms of signals such as pressure and electrical stimulation in human skin tactile perception can guide the design and optimization of multimodal bionic tactile systems. The conduction paths of electrical stimulation signals, the excitation modes of neurons, and the relationship between electrical stimulation and tactile perception can help to simulate the human skin's perception ability. This aids in the development of more accurate, sensitive, and biocompatible bionic electro-tactile skins, assisting the researcher in perceiving and providing various kinds of tactile information such as object shape, hardness, and texture.

At the same time, processing and fusing the different data acquired by sensors to generate comprehensive tactile feedback is one of the key technologies of a multimodal bionic tactile system. By integrating related tactile data from different sensors and applying signal processing methods such as data alignment, calibration, and fusion, as well as advanced feature processing technologies like feature extraction, filtering, and denoising, the bionic tactile system obtains more comprehensive and accurate tactile information. Therefore, studying data fusion and processing technology can enable bionic tactile systems to better understand and analyze tactile events, providing more reliable evidence for medical applications.

However, current bionic tactile systems face many challenges and technological barriers in practical applications, including the need for sensor integration and interoperability, the complexity of data fusion and processing, and the requirements for real-time, accurate bionic tactile systems. This Special Issue will focus on the research and development of sensor integration technology, data fusion algorithms, and electro-tactile skin perception mechanisms in order to promote the application of bionic tactile systems in the medical field and people's daily lives.

Dr. Yimin Zhou
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bionic tactile systems
  • multimodal sensor
  • sensor integration
  • data fusion
  • electro-tactile skin

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Published Papers (3 papers)

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Research

17 pages, 2195 KB  
Article
Collision-Free Robot Path Planning by Integrating DRL with Noise Layers and MPC
by Xinzhan Hong, Qieshi Zhang, Yexing Yang, Tianqi Zhao, Zhenyu Xu, Tichao Wang and Jing Ji
Sensors 2025, 25(20), 6263; https://doi.org/10.3390/s25206263 - 10 Oct 2025
Viewed by 486
Abstract
With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, [...] Read more.
With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, often under real-time constraints. Existing methods frequently struggle to balance efficiency, responsiveness, and safety, especially in the presence of continuously changing dynamic obstacles. While Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) have each shown promise in this domain, they also face limitations when applied individually—such as high computational demands or insufficient environmental exploration. To address these challenges, we propose a hybrid path planning framework that integrates an optimized DRL algorithm with MPC. We replace the Actor’s output with a learnable noisy linear layer whose mean and scale parameters are optimized jointly with the policy via backpropagation, thereby enhancing exploration while preserving training stability. TD3 produces stepwise control commands that evolve into a short-horizon reference trajectory, while MPC refines this trajectory through constraint-aware optimization to ensure timely obstacle avoidance. This complementary process combines TD3′s learning-based adaptability with MPC’s reliable local feasibility. Extensive experiments conducted in environments with varying obstacle dynamics and densities demonstrate that the proposed method significantly improves obstacle avoidance success rate, trajectory smoothness, and path accuracy compared to traditional MPC, standalone DRL, and other hybrid approaches, offering a robust and efficient solution for autonomous navigation in complex scenarios. Full article
(This article belongs to the Special Issue Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation)
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26 pages, 2120 KB  
Article
Continuous Vibration-Driven Virtual Tactile Motion Perception Across Fingertips
by Mehdi Adibi
Sensors 2025, 25(18), 5918; https://doi.org/10.3390/s25185918 - 22 Sep 2025
Viewed by 577
Abstract
Motion perception is a fundamental function of the tactile system, essential for object exploration and manipulation. While human studies have largely focused on discrete or pulsed stimuli with staggered onsets, many natural tactile signals are continuous and rhythmically patterned. Here, we investigate whether [...] Read more.
Motion perception is a fundamental function of the tactile system, essential for object exploration and manipulation. While human studies have largely focused on discrete or pulsed stimuli with staggered onsets, many natural tactile signals are continuous and rhythmically patterned. Here, we investigate whether phase differences between “simultaneously” presented, “continuous” amplitude-modulated vibrations can induce the perception of motion across fingertips. Participants reliably perceived motion direction at modulation frequencies up to 1 Hz, with discrimination performance systematically dependent on the phase lag between vibrations. Critically, trial-level confidence reports revealed the lowest certainty for anti-phase (180°) conditions, consistent with stimulus ambiguity as predicted by the mathematical framework. I propose two candidate computational mechanisms for tactile motion processing. The first is a conventional cross-correlation computation over the envelopes; the second is a probabilistic model based on the uncertain detection of temporal reference points (e.g., envelope peaks) within threshold-defined windows. This model, despite having only a single parameter (uncertainty width determined by an amplitude discrimination threshold), accounts for both the non-linear shape and asymmetries of observed psychometric functions. These results demonstrate that the human tactile system can extract directional information from distributed phase-coded signals in the absence of spatial displacement, revealing a motion perception mechanism that parallels arthropod systems but potentially arises from distinct perceptual constraints. The findings underscore the feasibility of sparse, phase-coded stimulation as a lightweight and reproducible method for conveying motion cues in wearable, motion-capable haptic devices. Full article
(This article belongs to the Special Issue Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation)
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24 pages, 23606 KB  
Article
Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment
by Xueyi He, Yimin Zhou, Haonan Liu and Wanfeng Shang
Sensors 2025, 25(8), 2364; https://doi.org/10.3390/s25082364 - 8 Apr 2025
Cited by 4 | Viewed by 2598
Abstract
This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling [...] Read more.
This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation. Full article
(This article belongs to the Special Issue Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation)
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