State-of-the-Art in Perception Technologies for Collaborative Robots

Ding, Xiaoxuan; Guo, Janli; Ren, Zijie; Deng, Pan

doi:10.1109/jsen.2021.3064588

Cited by 15 publications

(4 citation statements)

References 98 publications

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“…In [36], the main methods of sensor data fusion for cobot environment perception are classified as 'AI' or 'stochastic'. The latter group encompassing Bayesian filtering and Dempster-Shafer evidence theory, while the former includes fuzzy algorithms, neural networks, and fuzzy-neuro approaches.…”

Section: Industrial Roboticsmentioning

confidence: 99%

“…Kalman filtering has been applied for robot positioning [37][38][39], while the particle filter is shown to provide accurate positioning together with a consistent mapping of the 3D environment of the robot via simultaneous localisation and mapping [34,[40][41][42][43]. In their recent review, Ding et al [36] concluded that stochastic algorithm approaches are accurate and mature while AI approaches currently have limitations in practical cobot applications.…”

Section: Industrial Roboticsmentioning

confidence: 99%

“…Notably, the combination of a system model which predicts the progression of tool wear and/or part roughness together with measurement information from the process is shown to outperform machine learning models on their own [78]. In the case of robot perception, Bayesian filtering is also currently regarded as a more accurate and mature approach than AI-based methods such as ANN and neuro-fuzzy approaches [36].…”

Section: Relationship Between State Estimators and Machine Learning I...mentioning

confidence: 99%

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State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (`soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (`sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.

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Section: Industrial Roboticsmentioning

confidence: 99%

Section: Industrial Roboticsmentioning

confidence: 99%

Section: Relationship Between State Estimators and Machine Learning I...mentioning

confidence: 99%

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State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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“…This allows them to accurately perceive and avoid obstacles and achieve safe and efficient navigation and path planning. However, ultrasonic sonar sensors have the ability to locate targets in complex environments, but achieving accurate scene recognition requires the cooperation of multiple sensors, including visual and haptic sensors [1].…”

Section: Introductionmentioning

confidence: 99%

A bat biomimetic model for scenario recognition using echo Doppler information

Feng,

Chunyang,

Yuqing

et al. 2024

Bioinspir. Biomim.

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The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a 3D clutter model of the flying state of bat bio-sonar for bats emitting constant frequency (CF) signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a Convolutional neural network (CNN). The Short-time Fourier Transform (STFT) of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN's powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB Signal Noise Ratio (SNR). Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.

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Beyond the Horizon: Anticipating Future Challenges in the Field of Robotics

Detesan,

Moholea

2024

Mechanisms and Machine Science

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State-of-the-Art in Perception Technologies for Collaborative Robots

Cited by 15 publications

References 98 publications

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

A bat biomimetic model for scenario recognition using echo Doppler information

Beyond the Horizon: Anticipating Future Challenges in the Field of Robotics

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