In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques.
Integrated YOLOP Perception and Lyapunov-based Control for Autonomous Mobile Robot Navigation on Track
Graduate School of Science and Technology（科学与技术研究生学院）；Master’s Program in Science and Technology（科学技术硕士项目）；Green Science and Engineering Division（绿色科学与工程系）
We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. In order to increase the reliability and handle the failure cases of the expert policy, we combine with a policy extraction technique to transform the resulting policy into a decision tree format. The resulting decision tree has properties which we use to analyze and modify the policy and improve performance …
Politecnico di Milano（米兰理工大学）；CNRS-Université de Strasbourg（法国国家科学研究中心-斯特拉斯堡大学）；Delft University of Technology（代尔夫特理工大学）；Scuola Superiore Sant’Anna（圣安娜高等学校）；European Institute of Oncology (IRCCS)（欧洲肿瘤研究所（IRCCS））
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The development of tools to automate certain tasks could alleviate the physical and mental load of doctors during interventions, allowing them to focus on diagnosis and decision-making tasks. In this paper, we present a synergic solution for intraluminal navigation consisting of a 3D printed endoscopic soft robot that can move safely inside luminal structures. Visual servoing, based on Convolutional Neur…
We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinfo…
StepNav: Structured Trajectory Priors for Efficient and Multimodal Visual Navigation
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）
Norwegian University of Life Sciences（挪威生命科学大学）；University of Lincoln（林肯大学）
Sampling-Based Optimal(SBO) path planning has been mainly used for robotic arm manipulation tasks. Several research works have been carried out in order to evaluate performances of various SBO planners for arm manipulation. However, not much of work is available that highlights performances of SBO planners in context of mobile robot navigation in outdoor 3D environments. This paper evaluates performances of major SBO planners in Open Motion Planning Library(OMPL) for that purpose. Due to large number of existing SBO planners, experimenting and selecting a proper planner for a planning problem …
IIIT Hyderabad（国际信息技术研究所海得拉巴分校）；University of Tartu（塔尔图大学）
We present UrbanFly: an uncertainty-aware real-time planning framework for quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly is its ability to robustly plan directly on the sparse point clouds generated by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using the sparse point clouds to build an uncertainty-integrated cuboid representation of the environment through a data-driven monocular plane segmentation network. Our chosen world model provides faster distance queries than the more common voxel-grid representation, and UrbanFly leverages th…
Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
University of California, Riverside（加州大学河滨分校）；University of California, Berkeley（加州大学伯克利分校）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Stanford University（斯坦福大学）
The Bulldozer Technique: Efficient Elimination of Local Minima Traps for APF-Based Robot Navigation
Smart Automation and Communication Technologies (SACT), Research Institute of Sciences and Engineering, University of Sharjah, UAE（智能自动化与通信技术（SACT）、科学与工程研究院、沙迦大学，阿联酋）；Computer Engineering Department, University of Sharjah, UAE（计算机工程系、沙迦大学，阿联酋）
The Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Language Technology Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as pri…
The Hong Kong Polytechnic University（香港理工大学）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Self-supervised depth estimation has drawn much attention in recent years as it does not require labeled data but image sequences. Moreover, it can be conveniently used in various applications, such as autonomous driving, robotics, realistic navigation, and smart cities. However, extracting global contextual information from images and predicting a geometrically natural depth map remain challenging. In this paper, we present DLNet for pixel-wise depth estimation, which simultaneously extracts global and local features with the aid of our depth Linformer block. This block consists of the Linfor…
Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation
School of Computing, Clemson University（克莱姆森大学计算学院）；Stevens Institute of Technology（史蒂文斯理工学院）；School of Cultural and Social Studies, Tokai University（东海大学文化与社会研究学院）
LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
Technical University of Munich（慕尼黑技术大学）；Human-Centered Computing and Extended Reality Lab（以人为本计算与扩展现实实验室）；TUM University Hospital（慕尼黑大学医院）；Clinic for Orthopedics and Sports Orthopedics（骨科与运动医学诊所）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Learning Systems and Robotics Lab（学习系统与机器人实验室）
