In a recent paper published in the journal Machines, researchers from China presented the design of an intelligent indoor service robot capable of performing tasks like precise object recognition, autonomous path planning, and obstacle avoidance. Built upon the robot operating system (ROS) framework and leveraging deep learning techniques, the robot aims to tackle the challenges encountered by indoor service robots in dynamic and complex environments.
Study: Indoor Service Robot Design with ROS and Deep Learning. Image Credit: MONOPOLY919/Shutterstock.com
The research also compared the performance of different simultaneous localization and mapping (SLAM) algorithms and evaluated the robot’s navigation and detection accuracy through simulations and real-world experiments.
Background
Service robots are becoming increasingly popular and useful in various industries and domains, such as hospitality, healthcare, education, and entertainment. They play a vital role in assisting humans with tasks that are repetitive, hazardous, or demand high levels of precision and efficiency. However, indoor service robots face many challenges, such as adapting to dynamic and complex environments, identifying and locating target objects, planning optimal paths, and avoiding obstacles.
To address these challenges, researchers have been exploring the use of ROS and deep learning for service robot applications. ROS is an open-source framework that provides a set of tools and libraries for developing robot software. It supports various hardware platforms, sensors, and algorithms and facilitates communication and coordination among robot components.
Deep learning is a branch of machine learning that uses artificial neural networks to learn from large amounts of data and perform tasks such as image recognition, natural language processing, and speech synthesis. It has been widely applied to enhance the perception and intelligence of service robots.
About the Research
In this paper, the authors present a novel design for a service robot tailored to effectively and accurately recognize small objects—such as pens, glasses, and cups—and autonomously navigate while avoiding obstacles within office environments.
The robot comprises essential components including a differential drive chassis, a mechanical arm, an RGB camera, a laser rangefinder, an inertial measurement unit (IMU), and two primary controllers: a STM32F407VET6 microcontroller and an Nvidia Jetson Nano B01 embedded computer.
The proposed robot design utilizes the FreeRTOS real-time operating system on the STM32F407VET6 side and the ROS framework on the Jetson Nano side, operating on the Ubuntu 18.04 system. It incorporates the cutting-edge You Only Look Once version 3 (YOLOv3) algorithm, a state-of-the-art deep learning model for real-time object detection, to precisely identify and locate target objects.
Additionally, the design harnesses three distinct SLAM algorithms—GMapping, Hector-SLAM, and Cartographer—to construct and continuously update environmental maps while estimating their pose. SLAM performs the dual task of mapping an unknown environment and tracking the robot's location simultaneously. This capability is vital for effective navigation, exploration, and obstacle avoidance and can be realized through the utilization of diverse sensors and algorithms.
Moreover, the robot uses Dijkstra’s algorithm and the teb_local_planner algorithm to plan global and local paths, avoiding obstacles along the way based on maps and sensor data.
Additionally, the researchers conducted a series of simulations and real-world experiments to validate the performance and feasibility of the robot, including mapping, navigation, and target detection functionalities. They compared the results of different SLAM algorithms in terms of map quality, accuracy, and robustness and evaluated the object detection algorithm in terms of precision, recall, F1 score, and mean average precision (mAP).
Research Findings
The results demonstrated the new robot's adeptness in fulfilling mapping, navigation, and target detection objectives within indoor settings. It effectively generated detailed and precise maps leveraging SLAM algorithms, enabling seamless path planning and execution while adeptly circumventing obstacles. Moreover, the robot showcased impressive capabilities in detecting and identifying small objects with remarkable accuracy and efficiency through the utilization of the YOLOv3 algorithm.
Among all the utilized SLAM algorithms, the Cartographer algorithm emerged as the top performer, excelling in terms of map quality, accuracy, and robustness. It consistently produced smooth, error-minimized maps with impressive fidelity. Following closely, the Hector-SLAM algorithm demonstrated adeptness in managing fast motion and dynamic surroundings, albeit exhibiting susceptibility to drift and noise.
On the other hand, while the GMapping algorithm successfully rendered maps with distinct boundaries and intricate details, its performance was contingent upon precise parameter configurations and initial poses, making it susceptible to loop closure failures.
Moreover, the object detection algorithm, YOLOv3, achieved a high performance in terms of precision, recall, F1 score, and AP, underscoring its ability to accurately and consistently locate and classify small objects. Additionally, the authors compared the use of adaptive moment estimation (Adam) and stochastic gradient descent (SGD) optimization algorithms and found that the Adam algorithm performed better than the SGD algorithm in terms of mAP, suggesting that it was more suitable for object detection tasks.
Applications
The proposed indoor service robot has many potential applications in different domains and scenarios, such as:
- Hospitality: The robot can serve as a waiter, delivering food and drinks to customers, or as a receptionist, greeting and guiding guests.
- Healthcare: The robot can assist in patient care, such as monitoring vital signs, delivering medications, or providing companionship.
- Education: The robot can act as a tutor, teaching and testing students on various subjects, or as a librarian, helping users find and borrow books.
- Entertainment: The robot can entertain users with games, music, or stories or provide information and recommendations on various topics.
Conclusion
In summary, the novel robot demonstrated effectiveness in indoor service tasks by accurately recognizing small objects and navigating complex environments autonomously. Moving forward, researchers identified areas for improvement, including enhancing mechanical arm and camera stability, refining object detection algorithms, optimizing SLAM algorithms, and integrating additional sensors for better localization. They also suggested developing advanced functionalities like voice interaction and facial recognition.
Journal Reference
Liu, M.; Chen, M.; Wu, Z.; Zhong, B.; Deng, W. Implementation of Intelligent Indoor Service Robot Based on ROS and Deep Learning. Machines 2024, 12, 256. https://doi.org/10.3390/machines12040256, https://www.mdpi.com/2075-1702/12/4/256.
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Article Revisions
- Apr 24 2024 - Title changed from "Intelligent Indoor Robot for Object Detection, Mapping, and Navigation " to "Intelligent Indoor Robot for Object Detection, Mapping, and Navigation"