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ACO-GA-Based Optimization to Enhance Global Path Planning for Autonomous Navigation in Grid Environments

IEEE Transactions on Evolutionary Computation 2025年

作者： Heng, Han Rahiman, Wan Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang14300 Malaysia Universiti Sains Malaysia Engineering Campus Nibong Tebal Penang14300 Malaysia Daffodil Robotics Laboratory Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Exploring path planning techniques for autonomous vehicles to find the safest and quickest routes is fascinating. Among these techniques, generating grid-based maps is notable for simplifying the path planning process, especially in complex environments. This study introduces a novel optimization approach, the modified ant colony optimization and genetic algorithm (MACOGA), which is tailored for path planning in grid environments. MACOGA combines ant colony optimization (ACO) with genetic algorithm (GA) to efficiently navigate grid spaces. First, ACO identifies potential routes within a grid map, and GA optimizes them to find the optimal route. A key feature of MACOGA is its probabilistic prediction mechanism, which improves node selection by integrating the heuristic factors and probabilistic calculations of feasible potential paths, which significantly increases the likelihood of generating feasible initial paths and attains higher success rates in various environmental tests. The enhanced GA in the MACOGA incorporates ACO's pheromones and heuristic factors, and introduces adaptive probability crossover and mutation operations. Additionally, a path reconnection operation ensures favorable length and smoothness. To evaluate MACOGA's effectiveness, six planar models of varying sizes and complexities were studied. Results show MACOGA's superiority in path length and execution time, achieving the shortest path and planning time in several environments. The shortest path can be optimized to yield an improvement of up to 6%. Regarding runtime, even in the complex scenarios, it executes in just 0.3 seconds, outperforming other comparative algorithms. Furthermore, the success rate can reach 100% in relatively complex environments. © 2025 IEEE.

关键词： Ant colony optimization

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Energy-efficient Computation-In-Memory Architecture using Emerging Technologies

Energy-efficient Computation-In-Memory Architecture using Em...

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2023 International Conference on Microelectronics, ICM 2023

作者： Bishnoi, Rajendra Diware, Sumit Gebregiorgis, Anteneh Thomann, Simon Mannaa, Sara Deveautour, Bastien Marchand, Cedric Bosio, Alberto Deleruyelle, Damien O'Connor, Ian Amrouch, Hussam Hamdioui, Said Delft University of Technology Computer Engineering Laboratory Netherlands Munich Institute of Robotics and Machine Intelligence Technical University of Munich Chair of Ai Processor Design Germany University Lyon Ecl Insa Lyon Cpe Lyon Institut de Nanotechnologies de Lyon France

ISBN: (纸本)9798350380828

Deep Learning (DL) has recently led to remark-able advancements, however, it faces severe computation related challenges. Existing Von-Neumann-based solutions are dealing with issues such as memory bandwidth limitations and energy inefficiency. Computation-In-Memory (CIM) has the potential to address this problem by integrating processing elements directly into the memory architecture, reducing data movement and enhancing the overall efficiency of the system. In this work, we propose CIM architecture using three distinct emerging technologies. Firstly, a CIM architecture utilizing Ferroelectric Field-Effect Transistors (FeFET) is shown and the resulting errors from the analog compute scheme are injected into the emerging algorithm of Hyperdimensional Computing. Subsequently, we explore Vertical Nanowire Field-Effect Transistors (VNWFETs) based CIM within a 3D computing architecture, demonstrating improved energy efficiency and reconfigurability for CIM. Additionally, we improve the accuracy of the Resistive Random Access Memories (RRAM) based CIM architecture using two mapping-based solutions. These three technologies exhibit non-volatile characteristics, and when integrated into the CIM architecture, they yield significant advantages, including enhanced energy efficiency, reliability, and accuracy in computing processes. © 2023 IEEE.

关键词： Memory architecture

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词： Training Metalearning Visualization Semantic segmentation Semantics Prototypes Training data

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FEIF: Feature Excitation and Interactive Fusion for 6D Object Pose Estimation

FEIF: Feature Excitation and Interactive Fusion for 6D Objec...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Meng, Qiwei Zhu, Shiqiang Gu, Jianjun Lin, Zheyuan Zhong, Ling Song, Wei Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

In this paper, we present FEIF, a novel framework for 6D object pose estimation from a single RGBD image. Compared with previous approaches, this model fully leverages the complementary RGB and depth information through lay-erwise feature excitation and interactive fusion, thus improving the effectiveness and robustness of extracted features. The representative RGBD features can be subsequently applied for object keypoints prediction and pose computation. In addition, our FEIF includes a self-evaluation module to make robots aware of the confidence of pose estimation, so they are flexible to take the corresponding strategy for following tasks. Experiment results prove the SOTA performance of our model on LineMOD, Occlusion-LineMOD and YCB-Video datasets, and we also demonstrate its applicability and robustness in real-world robotic manipulation. © 2022 IEEE.

关键词：

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter 8

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Lin, Qiguang Yan, Chaojie Li, Qiang Ling, Yonggen Zheng, Yu Lee, Wangwei Wan, Zhaoliang Huang, Bidan Liu, Xiaofeng College of IoT Engineering Hohai University Jiangsu Key Laboratory of Special Robotic Technology Jiangsu Changzhou213022 China Zhejiang University Institute of Cyber-System and Control State Key Laboratory of Industrial Control and Technology Hangzhou China Bielefeld33619 Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350300178

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks. © 2023 IEEE.

关键词： Extended Kalman filters

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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Research Progress in Cell Classification Detection Based on Deep Learning 14

Research Progress in Cell Classification Detection Based on ...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Gai, Dongze Zhang, Chuang Zhang, Yiwei Guo, Jiaduo Zhang, Qi Qin, Hengshen Dang, Dan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 China School of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798331506056

This paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. This article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced. © 2024 IEEE.

关键词： Contrastive Learning

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HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Lin, Zheyuan Huang, Min Wang, Wen Qin, Mengjie Li, Te Wan, Minhong Gu, Jason Zhu, Shiqiang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR. © 2022 IEEE.

关键词： Human robot interaction

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PROSPECT: Precision Robot Spectroscopy Exploration and Characterization Tool

PROSPECT: Precision Robot Spectroscopy Exploration and Chara...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nathaniel Hanson Gary Lvov Vedant Rautela Samuel Hibbard Ethan Holand Charles DiMarzio Taşkın Padır Institute for Experiential Robotics Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts USA Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Electrical and Computer Engineering Department Northeastern University Boston Massachusetts USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an R 3 coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naïve point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment.

关键词： Reflectivity Point cloud compression Geometry Spectroscopy Three-dimensional displays Robot kinematics Robot sensing systems End effectors Planning Surface treatment

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An Augmented Reality Measurement Tool for the da Vinci Research Kit

An Augmented Reality Measurement Tool for the da Vinci Resea...

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International Symposium on Medical robotics (ISMR)

作者： Shiyue Wang Jing-Fen Wang Yubin Koh Haoying Zhou Anton Deguet Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Purdue University West Lafayette IN USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

We present an augmented reality surgical robotics console for the da Vinci Research Kit (dVRK) that integrates a real-time distance measurement tool to enhance spatial awareness during robotic-assisted surgical procedures through an interactive system. The system integrates an RViz-based interface with ROS topics, enabling virtual cursor visualization and interactive controls for precise measurements on tissue or phantom models. It supports two distinct measurement approaches: (1) a virtual measurement capability, where the operator uses the two Master Tool Manipulators (MTMs) to drive virtual 3D measurement cursors in the stereo display (a “masters as mice” mode), and (2) a physical measurement tool, where the user teleoperates one or more Patient Side Manipulators (PSMs) to touch two features and report the intervening distance. We conduct a user study on virtual (MTM) and teleoperation (PSM) modes to assess performance and accuracy and gather user feedback. The results show a Root Mean Square Error (RMSE) of 2.19 mm to 7.25 mm across different modes and tasks. This work underscores the importance of such systems for enhancing accuracy and usability in endoscopic surgery, where spatial awareness and intraoperative assessment are critical. Code is available at https://***/capricieuxV/RVinci_***.

关键词： Visualization Three-dimensional displays Medical robotics Accuracy Surgery Manipulators Real-time systems Usability Root mean square Augmented reality

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