2,482 research outputs found
A one decade survey of autonomous mobile robot systems
Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving
For the SLAM system in robotics and autonomous driving, the accuracy of
front-end odometry and back-end loop-closure detection determine the whole
intelligent system performance. But the LiDAR-SLAM could be disturbed by
current scene moving objects, resulting in drift errors and even loop-closure
failure. Thus, the ability to detect and segment moving objects is essential
for high-precision positioning and building a consistent map. In this paper, we
address the problem of moving object segmentation from 3D LiDAR scans to
improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D
Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately
segment the scene into moving and static objects, such as moving and static
cars. Different from the existing projected-image method, we process the raw 3D
point cloud and build a 3D convolution neural network for MOS task. In
addition, to make full use of the spatio-temporal information of point cloud,
we propose a point cloud residual mechanism using the spatial features of
current scan and the temporal features of previous residual scans. Besides, we
build a complete SLAM framework to verify the effectiveness and accuracy of
3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed
3D-SeqMOS method can effectively detect moving objects and improve the accuracy
of LiDAR odometry and loop-closure detection. The test results show our
3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the
proposed method to the SemanticKITTI: Moving Object Segmentation competition
and achieve the 2nd in the leaderboard, showing its effectiveness
A Study on Learning Social Robot Navigation with Multimodal Perception
Autonomous mobile robots need to perceive the environments with their onboard
sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation
decisions. In order to navigate human-inhabited public spaces, such a
navigation task becomes more than only obstacle avoidance, but also requires
considering surrounding humans and their intentions to somewhat change the
navigation behavior in response to the underlying social norms, i.e., being
socially compliant. Machine learning methods are shown to be effective in
capturing those complex and subtle social interactions in a data-driven manner,
without explicitly hand-crafting simplified models or cost functions.
Considering multiple available sensor modalities and the efficiency of learning
methods, this paper presents a comprehensive study on learning social robot
navigation with multimodal perception using a large-scale real-world dataset.
The study investigates social robot navigation decision making on both the
global and local planning levels and contrasts unimodal and multimodal learning
against a set of classical navigation approaches in different social scenarios,
while also analyzing the training and generalizability performance from the
learning perspective. We also conduct a human study on how learning with
multimodal perception affects the perceived social compliance. The results show
that multimodal learning has a clear advantage over unimodal learning in both
dataset and human studies. We open-source our code for the community's future
use to study multimodal perception for learning social robot navigation
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