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KRIS: A Robot for Material Transport in Relief Situations

KRIS: A Robot for Material Transport in Relief Situations

引用

2024 international symposium on Safety Security and Rescue robotics

作者： Mallwitz, Martin Weissenrieder, Daniel Origanti, Vamsi Krishna Becker, Tom Sixt, Milan Ralalage, Malika Navaratna Helapita Bernhard, Felix Schoo, Christian Genis, Miguel Martinez Bermpohl, Fabian Maas Genannt Yueksel, Mehmed Kirchner, Frank German Res Ctr Artificial Intelligence DFKI GmbH Robot Innovat Ctr D-28359 Bremen Germany Univ Bremen Dept Math & Informat D-28359 Bremen Germany Fed Agcy Tech Relief THW Bonn Germany

ISBN: (纸本)9798331510961;9798331510954

This paper presents KRIS, a semi-autonomous robot developed for material transport in disaster relief situations. In collaboration with the German Federal Agency for Technical Relief (THW) and the German Research Center for Artificial intelligence (DFKI), KRIS addresses the limitations of manual transport in hazardous environments. The robot is designed for enhanced mobility in rough terrain, featuring modes such as manual control, gesture-based person-following, and autonomous shuttle operation. The system is equipped with advanced sensors, including LiDAR and RGB-D cameras, and employs AI-driven gesture recognition for hands-free operation. Two field tests evaluated KRIS's usability in realistic disaster settings, demonstrating its ability to transport heavy equipment while reducing physical strain on rescue personnel. The findings highlight the potential of KRIS to improve efficiency and safety in disaster relief operations.

关键词： Disasters

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Fine-Tuning Point Cloud Transformers with Dynamic Aggregation

Fine-Tuning Point Cloud Transformers with Dynamic Aggregatio...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Fei, Jiajun Deng, Zhidong Tsinghua Univ Inst Artificial Intelligence Tsinghua Univ THUAI Beijing Natl Res Ctr Informat Sci & Technol BNRis State Key Lab Intelligent Technol & SystDept Com Beijing 100084 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Point clouds play an important role in 3D analysis, which has broad applications in robotics and autonomous driving. The pre-training fine-tuning paradigm has shown great potential in the point cloud domain. Full fine-tuning is generally effective but leads to a heavy storage and computational burden, which becomes inefficient and unacceptable as the size of pre-trained models scales. Although efficient fine-tuning approaches have significant progress in other domains, they generally perform worse for point clouds. To overcome this dilemma, we revisit the official Point-MAE implementation and find the critical role of aggregation in fine-tuning performances. Inspired by such discoveries, we propose a novel dynamic aggregation (DA) method to replace previous static aggregation like mean or max pooling for pre-trained point cloud Transformers. Besides standard metrics such as accuracy or mIoU, we evaluate the number of tunable parameters and additional FLOPs for a fair comparison of our method to different fine-tuning approaches. We construct several DA variants and validate them through extensive experiments. Experimental results demonstrate that DA has competitive performances against full fine-tuning and other efficient fine-tuning approaches. The code is publicly available at https://***/JaronTHU/DynamicAggregation.

关键词： Distribution transformers

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TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation

TSCM: A Teacher-Student Model for Vision Place Recognition U...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Shen, Yehui Liu, Mingmin Lu, Huimin Chen, Xieyuanli Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China SIASUN Robot & Automat Co Ltd Changsha Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Visual place recognition (VPR) plays a pivotal role in autonomous exploration and navigation of mobile robots within complex outdoor environments. While cost-effective and easily deployed, camera sensors are sensitive to lighting and weather changes, and even slight image alterations can greatly affect VPR efficiency and precision. Existing methods overcome this by exploiting powerful yet large networks, leading to significant consumption of computational resources. In this paper, we propose a high-performance teacher and lightweight student distillation framework called TSCM. It exploits our devised cross-metric knowledge distillation to narrow the performance gap between the teacher and student models, maintaining superior performance while enabling minimal computational load during deployment. We conduct comprehensive evaluations on large-scale datasets, namely Pittsburgh30k and Pittsburgh250k. Experimental results demonstrate the superiority of our method over baseline models in terms of recognition accuracy and model parameter efficiency. Moreover, our ablation studies show that the proposed knowledge distillation technique surpasses other counterparts. The code of our method has been released at https://***/nubot-nudt/TSCM.

关键词： Students

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Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection

Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Vir...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Park, Konyul Kim, Yecheol Koh, Junho Park, Byungwoo Choi, Jun Won Hanyang Univ Dept Artificial Intelligence Seoul 04763 South Korea Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Seoul Natl Univ Coll Liberal Studies Dept Elect & Comp Engn Seoul 08826 South Korea

ISBN: (纸本)9798350384581;9798350384574

Developing high-performance, real-time architectures for LiDAR-based 3D object detectors is essential for the successful commercialization of autonomous vehicles. Pillar-based methods stand out as a practical choice for onboard deployment due to their computational efficiency. However, despite their efficiency, these methods can sometimes underperform compared to alternative point encoding techniques such as Voxel-encoding or PointNet++. We argue that current pillar-based methods have not sufficiently captured the fine-grained distributions of LiDAR points within each pillar structure. Consequently, there exists considerable room for improvement in pillar feature encoding. In this paper, we introduce a novel pillar encoding architecture referred to as Fine-Grained Pillar Feature Encoding (FG-PFE). FG-PFE utilizes Spatio-Temporal Virtual (STV) grids to capture the distribution of point clouds within each pillar across vertical, temporal, and horizontal dimensions. Through STV grids, points within each pillar are individually encoded using Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE). These encoded features are then aggregated through an Attentive Pillar Aggregation method. Our experiments conducted on the nuScenes dataset demonstrate that FG-PFE achieves significant performance improvements over baseline models such as PointPillar, CenterPoint-Pillar, and PillarNet, with only a minor increase in computational overhead.

关键词： Spatio-temporal data

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Explainability of deep learning models in medical image classification 22

Explainability of deep learning models in medical image clas...

引用

ieee Joint 22nd international symposium on computational intelligence and Informatics / 8th ieee international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics (CINTI-MACRo)

作者： Kolarik, Michal Sarnovsky, Martin Paralic, Jan Butka, Peter Tech Univ Kosice FEI Dept Cybernet & Artificial Intelligence Kosice Slovakia

ISBN: (纸本)9798350398823

The ability to explain the reasons for one's decisions to others is an important aspect of being human intelligence. We will look at the explainability aspects of the deep learning models, which are most frequently used in medical image processing tasks. The Explainability of machine learning models in medicine is essential for understanding how the particular ML model works and how it solves the problems it was designed for. The work presented in this paper focuses on the classification of lung CT scans for the detection of COVID-19 patients. We used CNN and DenseNet models for the classification and explored the application of selected visual explainability techniques to provide insight into how the model works when processing the images.

关键词： Deep learning explainability XAI image classification

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Evaluation of the Use of an Intelligent System in the Calibration of a Refined Car-Following Model 22

Evaluation of the Use of an Intelligent System in the Calibr...

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作者： Pop, Madalin-Dorin Prostean, Octavian Micea, Mihai V. Politehn Univ Timioara Comp & Informat Technol Dept Timisoara Romania Politehn Univ Timioara Automat & Appl Informat Dept Timisoara Romania

ISBN: (纸本)9798350398823

This paper aims to extract the computational logic of a hybrid method for online calibration of car-following models and apply it to a refined car-following model that incorporates the behavior of vehicles moving on the adjacent traffic lanes. This calibration method combines the concept of Kalman filters with the Takagi-Sugeno Fuzzy Inference System (T-S FIS). Furthermore, this paper analyzes the influence of the lane change behavior on the calibration process. The testing of the hybrid calibration method in the case of a refined car-following model uses real traffic data and it is followed by a discussion of the results based on day/night traffic behaviors.

关键词： computational intelligent system fuzzy inference refined car-following model Takagi-Sugeno Kalman filter online calibration lane change behavior

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A Gesture-Based Communication System for Firefighters During Search and Rescue Missions

A Gesture-Based Communication System for Firefighters During...

引用

2024 international symposium on Safety Security and Rescue robotics

作者： Mabulu, Katiso Vainqueur, Ben-Oni Padir, Taskin

ISBN: (纸本)9798331510961;9798331510954

Fire departments have primarily utilized radios for communication since the 1980s. Although this method of communication may suffice for non-emergency calls, in the case of a search and rescue, radios have been found to hinder efficient and safe communication. This paper presents a gesture based communication system that reduces the amount of time and effort for communication. There is no standard fire suppressive glove used by firefighters, therefore four gloves were trained with three different predictive models (Random Forrest (RF), k-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM)). LSTM neural network model produced the most accurate results with accuracy percentages between 97.38% and 98.94% for all four glove types. Furthermore, a gesture scheme was designed and tested for a search and rescue task, and compared against radio communication. The results showed that the average search time was reduced by 9.30s. Additionally, a survey was conducted by firefighters stating their preference of the gesture system over radio communication and desire to integrate the system into their standard gear.

关键词： Human-robot interaction Artificial intelligence

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Predicting against the Flow: Boosting Source Localization by Means of Field Belief Modeling using Upstream Source Proximity

Predicting against the Flow: Boosting Source Localization by...

引用

ieee international Conference on robotics and automation (ICRA)

作者： Busch, Finn L. Bauschmann, Nathalie Haddadin, Sami Seifried, Robert Duecker, Daniel A. Hamburg Univ Technol Inst Mech & Ocean Engn Hamburg Germany KTH Royal Inst Technol Div Robot Percept & Learning RPL Stockholm Sweden Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (纸本)9798350384581;9798350384574

Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e. g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i. e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard resources. We propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF's concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief's concentration and flow field, i. e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based methods. We demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10 Hz on a RaspberryPi 4B.

关键词： Markov processes

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Embodied AI Through Cloud-Fog Computing: A Framework for Everywhere intelligence 33

Embodied AI Through Cloud-Fog Computing: A Framework for Eve...

引用

33rd international symposium on Industrial Electronics (ISIE)

作者： Hu, Dongxiao Lan, Dapeng Liu, Yu Ning, Jiahong Wang, Jia Yang, Yun Pang, Zhibo Xian Jiaotong Liverpool Univ Sch Adv Technol Xian Peoples R China Chinese Acad Sci Shenyang Shenyang Inst Automat Shenyang Peoples R China Linkoping Univ Dept Sci & Technol Linkoping Sweden Dalian Marine Univ Dalian Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming Yunnan Peoples R China KTH Royal Inst Technol Dept Intelligent Syst Stockholm Sweden

ISBN: (纸本)9798350394085;9798350394092

Embodied AI represents a crucial step towards achieving Artificial General intelligence (AGI). The next paradigm of Embodied AI involves physical embodiment, enhanced perception capabilities, and adaptive automation. This advances the field significantly, paving the way for broader expansion. Despite the significant progress, existing computing frameworks, like local computation or cloud computing, struggle to meet the substantial demands of Embodied AI. The Cloud-Fog Embodied framework, namely based on CFA (cloud-fog automation) offers a promising solution to address these challenges. Our goal is to drive integration across multiple domains, including AI, robotics and industrial production, to tackle multifaceted challenges and seize opportunities to achieve AGI in the future.

关键词： Embodied AI agent cloud-fog automation pretrained foundation models

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A Rapidly-Exploring Random Tree Algorithm with Reduced Random Map Size 9

A Rapidly-Exploring Random Tree Algorithm with Reduced Rando...

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9th international Conference on automation, robotics and Applications (ICARA)

作者： Lonklang, Aphilak Botzheim, Janos Eotvos Lorand Univ Dept Artificial Intelligence Fac Informat H-1117 Budapest Hungary

ISBN: (纸本)9781665489218

Mobile robots have been widely used in automated factory applications such as raw material delivery and product storage transportation. Path planning algorithms have been proposed to generate a feasible global approach. The path result must be free from obstacle regions and shortest. Previously we proposed an Improved Rapidly-Exploring Random Tree (Improved RRT*) algorithm. The algorithm consists of the pre-processing step for feasible mapping, primary processing with RRT* for path generating, and post-processing with Bacterial Mutation and Node Deletion operators. This paper aims to improve further the capability of the Improved RRT* algorithm by reducing the overall computation time. The proposed method reduces the complexity of the random map after each iteration by deleting the used nodes. In this way, the computational time could be reduced.

关键词： Path Planning Algorithm RRT Bacterial Mutation Node Deletion

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