Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning

Chatterjee, Sayantan; Zunjani, Faheem Hassan; Nandi, Gora Chand

doi:10.1109/iccar49639.2020.9108054

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…In the domain of mobile robotics Chatterjee et al [24], utilized YOLO for detecting objects for a robot in multiple indoor experiment scenarios. One of the problems in robotics is limited computation capability, which was addressed by Luo et al [25], were developed a Raspberry PI-based robot to perform real vision tasks and object detection.…”

Section: Related Workmentioning

confidence: 99%

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Singh¹,

Kapoor²,

Abouhawwash³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Navigation is an essential skill for robots. It becomes a cumbersome task for the robot in a human-populated environment, and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots. Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety. With the advancement in robotics technology, the true use cases of robots in the tourism and hospitality industry are expanding in number. There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot. A robotic platform named "PI" has been designed, which incorporates proximity and vision sensors. The robot utilizes a real-time object recognition algorithm based on the You Only Look Once (YOLO) algorithm to detect objects and humans during navigation. This study is aimed towards evaluating human experience, for which we conducted a study among 36 participants to explore the perceived social presence, role, and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor. The participants' responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior. Further, this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

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Section: Related Workmentioning

confidence: 99%

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Singh¹,

Kapoor²,

Abouhawwash³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Cruz et al [48] could additionally estimate player poses, goal locations and other key pitch features using intensity images alone. Due to the low on-board computational power of the humanoids, others have used fast, low power external mobile GPU boards such as the Nvidia Jetson to aid inference [47,49].…”

Section: Localization Mapping and Slammentioning

confidence: 99%

Perception for Humanoid Robots

Roychoudhury,

Khorshidi,

Agrawal

et al. 2023

Curr Robot Rep

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Purpose of Review The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.

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“…Thanks to the rapid development in deep learning technology, various types of artificial intelligence have been implemented to human care robot in order to make them comprehend situations of users and take appropriate actions corresponding to the situations automatically [2,3]. For example, we could implement object detection algorithm on a robot with cameras to observe and understand the environment and circumstance [4][5][6]. However, visual intelligence provides limited understanding on circumstances around the robot, as it can only recognizes events within visible regions and fails to recognize events in the blind sight of the camera.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

Kim,

Nam,

Choi

et al. 2024

IEEE Access

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In real-life situations where human care robots are deployed, there are custom sound events whose acoustic characteristics change depending on the user's choice unlike general sound events so that the human care robots cannot recognize custom sound events correctly in a conventional way. To solve this critical problem, a real-time sound event recognition system with customization process is proposed. The human care robot collects custom sound samples of a specific user and customizes a sound event recognition model. The overfitting-based customized model shows the best recognition performance by improving F-scores by 66.4% on average compared to the conventional recognition model. After the customization process, the human care robot performs a real-time sound recognition by consistently streaming robot's real-time microphone signals into the overfitting-based customized SER model. In this process, an optimized overlap is applied on subsequent audio inputs on SER to achieve sufficiently fast response and robust performance. As a pilot test of the human care robot implemented in actual environment, the real-time sound recognition system shows the best average F-score of 0.982 with 75% overlap for sound events including custom sounds. This pilot test result confirms that the real-time sound recognition system with customization process can be successfully applied to human care robots to respond to the custom sounds.INDEX TERMS Sound event recognition, human care robot, custom sound event, real-time system

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Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning

Cited by 14 publications

References 18 publications

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Perception for Humanoid Robots

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

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