2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)最新文献_第5页

Continual Learning of Vacuum Grasps from Grasp Outcome for Unsupervised Domain Adaption 无监督域自适应真空抓取结果的持续学习

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092970

Maximilian Gilles, Vinzenz Rau

{"title":"Continual Learning of Vacuum Grasps from Grasp Outcome for Unsupervised Domain Adaption","authors":"Maximilian Gilles, Vinzenz Rau","doi":"10.1109/RAAI56146.2022.10092970","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092970","url":null,"abstract":"Training grasping robots in isolation can result in large performance gaps when deploying to real world applications. This problem gains in importance when synthetic data is used for training. To meet the desired performance for a specific use-case, fine-tuning the model's parameters to account for the persistent domain shift between training and application data is usually required. To speed up deployment time and reduce costs, a picking robot should be able to continually adapt to its new domain by incorporating knowledge generated during operation. The proposed method enables a robot to perform domain adaption from source domain to target domain data completely selfsupervised by continually adapting its model's weights to the new target domain, relying only on feedback about grasp success or failure. It is based on two core ideas: 1) extrapolation of the suctionable area around a conducted grasp based on local curvature analysis of sensor data, and 2) uncertainty-weighted knowledge distillation-based pseudo labels for ambiguous background pixels for which no information about graspability is available from the current experiment. Extensive sim-to-real experiments on the challenging MetaGraspNet dataset show that the proposed method improves grasp success rate in average by more than 13% on real world scenes compared to purely synthetic training data.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134228631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 基于自适应网络模糊推理系统的水培自动控制

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092958

N. Surantha, Vito Vincentdo

{"title":"NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System","authors":"N. Surantha, Vito Vincentdo","doi":"10.1109/RAAI56146.2022.10092958","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092958","url":null,"abstract":"The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133618368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Steering of Flexible Needles Using an LSTM Encoder with Model Predictive Control 基于模型预测控制的LSTM编码器的柔性针转向

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092964

Chris Morley, Rajni V. Patel

{"title":"Steering of Flexible Needles Using an LSTM Encoder with Model Predictive Control","authors":"Chris Morley, Rajni V. Patel","doi":"10.1109/RAAI56146.2022.10092964","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092964","url":null,"abstract":"This work presents a method of using recurrent neural networks (RNNs) in combination with model predictive control to determine an optimal control strategy for needle manipulation for deep tissue applications. The paper discusses creating a needle insertion model from experimental data that can then be used to generate data for training the proposed network. The RNN makes no assumptions about needle-tissue interaction, instead it learns the dynamics of the interaction from simulated and experimental data. It is shown in the paper how deep recurrent neural networks can create a simple cost function enabling model predictive control to determine an optimal sequence of needle manipulations. Simulation results show that the proposed control structure can accurately predict the effect of current control actions on future trajectory. Simulation results indicate that the proposed control strategy is able to determine an optimal control strategy within a few time steps of the simulation initializing, while requiring only one rotation to enable a needle to be steered to within 1.1mm of the desired target.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134102922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Addressing Different Goal Selection Strategies In Hindsight Experience Replay With Actor-Critic Methods For Robotic Hand Manipulation 机器人手操作中不同目标选择策略的后见经验回放

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092979

Ayman Shams, Thomas Fevens

引用次数: 0

AI Based Biometrics Recognition with a Hyperspectral Image Sensor 基于人工智能的高光谱图像传感器生物特征识别

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092954

Ryo Nakazawa, C. Premachandra

{"title":"AI Based Biometrics Recognition with a Hyperspectral Image Sensor","authors":"Ryo Nakazawa, C. Premachandra","doi":"10.1109/RAAI56146.2022.10092954","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092954","url":null,"abstract":"In information security, facial recognition technology is one of the most familiar authentication technologies in our daily life. However, the recent development in the field of Artificial Intelligence has made it easy to generate a human face image from a small number of images, which poses a threat to individual information security. This is known as deep faking, and is a problem for future information security. As a solution for this issue, we develop a face recognition technique based on highdimensional images using a hyperspectral sensor (HSI). Unlike ordinary two-dimensional sensors, the high-dimensional data acquired here is a huge cube-shaped data with spectral wavelength information. In order to perform authentication using highdimensional images, we generated images for each wavelength band by dividing the wavelength information into pseudo-bands and created classification models for each band. Inference is performed on a single high-dimensional image using multiple trained classification models, and authentication is based on the majority vote of the models. We confirmed the effectiveness of these methods through validation experiments.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RRDV: Robots Rendez-Vous Detection Using Time-Synchronized Ultrasonic Sensors 基于时间同步超声传感器的机器人交会探测

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092965

Trifun Savic, Said Alvarado-Marin, F. Maksimovic, T. Watteyne

{"title":"RRDV: Robots Rendez-Vous Detection Using Time-Synchronized Ultrasonic Sensors","authors":"Trifun Savic, Said Alvarado-Marin, F. Maksimovic, T. Watteyne","doi":"10.1109/RAAI56146.2022.10092965","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092965","url":null,"abstract":"In this paper we propose RRDV, a system for robot-to-robot encounter detection. We use low-cost ultrasound sensor and time-synchronized mobile robots to detect when two robots are facing one another. Ultrasound ranging is triggered by the control application on a computer. The application sends a ranging command to the gateway, which broadcasts it to the mobile robots over the radio. Robots synchronize their ultrasound trigger pin with the start of frame event and send back the notifications with measured distances using Time-Division Multiple Access (TDMA). The system then finds the encounters by searching for timestamps where the difference in distance reported by two robots is less then 1 cm. In the current implementation, the system achieves a 20 Hz distance measurement update rate. RRDV is validated experimentally using 5 mobile robots which are controlled by the users and moved randomly. We implemented a Computer Vision (CV) algorithm for tracking mobile robots as they move and detect when they are facing one another. The CV algorithm is used as the ground truth for the experimental evaluation. The results show 96.7% successfully detected robot encounters, when the duration of the encounter is more than 5 s.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128913560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Reinforcement Learning Approach for Non-homogeneous Patrolling using Wi-Fi Fleet-restricted Autonomous Vehicles 基于Wi-Fi车队限制的自动驾驶车辆非同质巡逻的深度强化学习方法

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI) Pub Date : 2022-12-09 DOI: 10.1109/RAAI56146.2022.10092959

S. Luis, D. Reina, S. T. Marín

{"title":"A Deep Reinforcement Learning Approach for Non-homogeneous Patrolling using Wi-Fi Fleet-restricted Autonomous Vehicles","authors":"S. Luis, D. Reina, S. T. Marín","doi":"10.1109/RAAI56146.2022.10092959","DOIUrl":"https://doi.org/10.1109/RAAI56146.2022.10092959","url":null,"abstract":"The use of intelligent autonomous vehicles to monitor natural phenomena involves the optimization of multiple policies that must comply with physical restrictions of the environment. In the patrolling problem, typically addressed in the environmental surveillance of natural scenarios, it is required to fulfill the non-homogeneous coverage of an unknown scalar map, with limitations of navigable areas and communication. This work presents a framework based on deep reinforcement learning to deal with communication restrictions for online route planning and patrolling with multiple vehicles. This algorithm, based on the Deep Q-Learning algorithm, using a customized reward function and a fleet-informed deep network, is able to optimize every vehicle policy to maintain each vehicle’s distance from another within the limits of its wireless communication protocol (WiFi). The results show better performance than other path planning heuristics, while being a model-free approach and providing an effective method to use in similar patrolling scenarios.","PeriodicalId":190255,"journal":{"name":"2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128547178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0