Environment Perception Technology for Intelligent Robots in Complex Environments: A Review

Gao, Jun; Jiao, Yi; Liu, Peng; Xu, Changshui

doi:10.1109/ccisp55629.2022.9974277

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Machine-learning approaches, specifically probabilistic methods like Bayesian filtering, play a pivotal role in combining information from various sensors (e.g., cameras, LiDAR, IMUs) to generate a coherent representation of the environment [148][149][150]. Moreover, Kalman Filters and their variants (Extended Kalman Filter, Unscented Kalman Filter) are extensively used for sensor fusion, enabling estimation of the robot's state and the environment's structure [151][152][153]. Additionally, Particle Filters provide a non-parametric approach to handle non-linear and non-Gaussian distributions [154,155].…”

Section: Sensor Fusion For Multi-modal Perceptionmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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This paper provides a comprehensive review of the integration of advanced power management systems and learning techniques in the field of robotics. It identifies the critical roles these areas play in reshaping the capabilities of robotic systems across diverse applications. To begin, it highlights the significance of efficient power usage in modern robotics. The paper explains how advanced power converters effectively control voltage, manage current and shape waveforms, thereby optimizing energy utilization. These converters ensure that robotic components receive the precise voltage levels they require, leading to improved motor performance and enabling precise control over motor behavior. Consequently, this results in extended operational times and increased design flexibility. Furthermore, the review explores the integration of learning approaches, emphasizing their substantial impact on robotic perception, decision-making and autonomy. It discusses the application of techniques such as reinforcement learning, supervised learning and unsupervised learning, showcasing their applications in areas like object recognition, semantic segmentation, sensor fusion and anomaly detection. By utilizing these learning methods, robots become more intelligent, adaptable and capable of autonomous operation across various domains. By examining the interaction between advanced power management and learning integration, this review anticipates a future where robots operate with increased efficiency, adapt to various tasks and drive technological innovation across a wide range of industries.

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Section: Sensor Fusion For Multi-modal Perceptionmentioning

confidence: 99%

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Singh,

Kurukuru,

Khan

2023

Energies

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“…In robotics, a 4D mmw radar can be integrated as a perception sensor to enable robots to detect and track objects, relocate, and avoid obstacles in their environment [55][56][57][58], which is similar to the applications in autonomous driving. In addition, some researchers have also applied this technology to develop human-following robots [59] and estimate the velocity of robots in visually degraded environments [60].…”

Section: Application In Roboticsmentioning

confidence: 99%

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

et al. 2023

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Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed.

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“…Another review by Wu et al (2022) discusses the limitations of vision-based environmental perception technologies. It emphasizes the importance of multi-sensor fusion for enhanced adaptability and performance in complex, unstructured conditions, offering insights into various application scenarios, datasets, and sensor fusion methods.…”

Section: Significance Of the Surveymentioning

confidence: 99%

Intelligent Systems in Motion

Cai,

Qin,

et al. 2023

International Journal on Semantic Web and Information Systems

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Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

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Environment Perception Technology for Intelligent Robots in Complex Environments: A Review

Cited by 6 publications

References 28 publications

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Advanced Power Converters and Learning in Diverse Robotic Innovation: A Review

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

Intelligent Systems in Motion

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