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Intelligent Robots: Control and Sensing

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Special Issue Information

Dear Colleagues,

The control of today’s intelligent robots is both complex and challenging, and depends on the effective sensing of the environment. The sensing of the environment—acquiring the information of the environment using sensors—is essential to control, so as to enable corresponding actions to be made. Robot control and sensing are highly interactive processes, playing an increasingly significant role in the development of intelligent robots, amid progress in artificial intelligence. This Special Issue will be dedicated to new ideas and research in the broad field of robot sensing and control, and their practical applications.

This Special Issue will publish high-quality, original research papers including, but not limited to, the following topics:

Robot perception;
Robotic sensing;
Robot control;
Applied machine learning for robotic systems;
Swarm robot control.

Dr. Ting Zou
Guest Editor

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13 pages, 16914 KB

Open AccessArticle

Traversal by Touch: Tactile-Based Robotic Traversal with Artificial Skin in Complex Environments

by Adam Mazurick and Alex Ferworn

Sensors 2025, 25(21), 6569; https://doi.org/10.3390/s25216569 - 25 Oct 2025

Viewed by 191

Abstract

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence. Full article

(This article belongs to the Special Issue Intelligent Robots: Control and Sensing)

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24 pages, 9679 KB

Open AccessArticle

Control Gain Determination Method for Robust Time-Delay Control of Industrial Robot Manipulators Based on an Improved State Observer

by Yu Chen, Jianwan Ding, Tianchang Xu and Yanbing Liu

Sensors 2025, 25(18), 5812; https://doi.org/10.3390/s25185812 - 17 Sep 2025

Viewed by 478

Abstract

High-precision control of robotic manipulators plays a vital role in improving the efficiency and quality of industrial manufacturing. However, the inherent nonlinear and time-varying characteristics of robotic systems make high-accuracy control a challenging task, and external noise interference further complicates reliable state estimation. Conventional time-delay control methods often involve computationally intensive procedures for gain determination and are limited in their ability to suppress noise effectively. To overcome these limitations, this paper proposes a robust time-delay control strategy based on an improved state observer. By deriving a linearized form of the dynamic model, an offline computation scheme for control gain determination is developed, which eliminates the need for additional tuning parameters and simplifies the design process. Furthermore, the proposed state observer integrates model reference estimation with noise suppression techniques, enabling accurate acquisition of joint states and improving system robustness under noisy conditions. Experimental results validate the effectiveness of the proposed method, showing that it can efficiently determine control gains and significantly outperform existing advanced approaches in terms of trajectory tracking accuracy and overall control performance. Full article

(This article belongs to the Special Issue Intelligent Robots: Control and Sensing)

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