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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Time-to-Line Crossing Enhanced End-to-End Autonomous Driving Framework

Time-to-Line Crossing Enhanced End-to-End Autonomous Driving...

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International Conference on Intelligent Transportation

作者： Chanyoung Jung Hyunki Seong David Hyunchul Shim School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea KAIST Robotics Program Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

End-to-end autonomous driving approach, which directly maps raw input images to vehicle control commands using a deep neural network, is gaining considerable attention from both academia and industry. Researchers have conducted studies on this subject over the past few years. However, they have focused on designing network architecture, and evaluated performance only with root mean square error (RMSE), which did not account for the temporal dynamics of autonomous driving. In this paper, we propose a time-to-line crossing (TLC) enhanced end-to-end driving framework. The proposed framework is original to the industry in three ways. First, for the fine-labeled training dataset for end-to-end autonomous driving, the TLC based label correction algorithm is applied to reduce the inaccuracies of driver action, which is used as a ground truth. Second, we designed a novel deep neural network model based on bi-directional convolutional long short-term memory (bi-CLSTM) which can sufficiently encode the spatial and temporal features in the input image sequence. Third, in addition to the RMSE evaluation metric, we validated the performance of the end-to-end driving model using TLC, the advanced driving support system (ADAS), and a driver performance indicator. We integrated the proposed framework with existing end-to-end driving models on a full-scale autonomous vehicle in the experimental portion of our study. The results show that the proposed framework is valid and that our network model outperforms the existing models in terms of both RMSE and TLC.

关键词： Feature extraction Autonomous vehicles Training Vehicles Network architecture Data mining Wheels

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Opportunistic Synthesis in Reactive Games under Information Asymmetry

Opportunistic Synthesis in Reactive Games under Information ...

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IEEE Annual Conference on Decision and Control

作者： Abhishek N. Kulkarni Jie Fu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Faculty of the Department of Electrical and Computer Engineering with affiliation to Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Reactive synthesis is a class of methods to construct a provably-correct control system, referred to as a robot, with respect to a temporal logic specification in the presence of a dynamic and uncontrollable environment. This is achieved by modeling the interaction between the robot and its environment as a two-player zero-sum game. However, existing reactive synthesis methods assume both players to have complete and symmetrical information, which is not the case in many strategic interactions. In this paper, we use a variant of hypergames to model the interaction between the robot and its environment;where the latter has incomplete information about the specification of the robot. We propose a novel method of opportunistic synthesis defined over the hypergame model to identify a subset of hypergame states from where the robot can leverage the asymmetrical information to achieve a better outcome, which is not possible if both players have symmetrical and complete information. By assuming the environment to play a stochastic strategy in its perceived sure-winning and sure-losing regions of the game, we show that by following the opportunistic strategy, the robot is ensured to only improve the outcome of the game-measured by satisfaction of sub-specifications-whenever an opportunity becomes available. We demonstrate the correctness and optimality of this method using a robot motion planning example in the presence of an adversary.

关键词： Robots Games Task analysis Computational modeling Game theory Planning Stochastic processes

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Adaptive Traffic Signal Control for Developing Countries Using Fused Parameters Derived from Crowd-Source Data

arXiv

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arXiv 2022年

作者： Mishra, Sumit Singh, Vishal Gupta, Ankit Bhattacharya, Devanjan Mudgal, Abhisek The Robotics Program Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Member of Technical Staff VMware Inc Palo AltoCA United States Department of Civil Engineering Indian Institute of Technology Banaras Hindu University Varanasi India Marie Sklodowska-Curie Actions TRAIN@ED School of Law School of Informatics University of Edinburgh United Kingdom Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an inexpensive alternative for traffic sensing and tracking technologies. © 2022, CC BY.

关键词： Crowdsourcing

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Low-latency perception in off-road dynamical low visibility environments

arXiv

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arXiv 2020年

作者： Alves, Nelson Ruiz, Marco Reis, Marco Cajahyba, Tiago Oliveira, Davi Barreto, Ana Simas Filho, Eduardo F. de Oliveira, Wagner L.A. Schnitman, Leizer Monteiro, Roberto L.S. Electrical Engineering Program Federal University of Bahia Salvador Brazil Brazilian Institute of Robotics SENAI CIMATEC Salvador Brazil Federal University of Bahia PPGM Salvador Brazil Computational Modeling and Industrial Technology Program SENAI CIMATEC Salvador Brazil

This work proposes a perception system for autonomous vehicles and advanced driver assistance specialized on unpaved roads and off-road environments. In this research, the authors have investigated the behavior of Deep Learning algorithms applied to semantic segmentation of off-road environments and unpaved roads under differents adverse conditions of visibility. Almost 12,000 images of different unpaved and off-road environments were collected and labeled. It was assembled an off-road proving ground exclusively for its development. The proposed dataset also contains many adverse situations such as rain, dust, and low light. To develop the system, we have used convolutional neural networks trained to segment obstacles and areas where the car can pass through. We developed a Configurable Modular Segmentation Network (CMSNet) framework to help create different architectures arrangements and test them on the proposed dataset. Besides, we also have ported some CMSNet configurations by removing and fusing many layers using TensorRT, C++, and CUDA to achieve embedded real-time inference and allow field tests. The main contributions of this work are: a new dataset for unpaved roads and off-roads environments containing many adverse conditions such as night, rain, and dust;a CMSNet framework;an investigation regarding the feasibility of applying deep learning to detect region where the vehicle can pass through when there is no clear boundary of the track;a study of how our proposed segmentation algorithms behave in different severity levels of visibility impairment;and an evaluation of field tests carried out with semantic segmentation architectures ported for real-time inference. The proposed dataset (named Kamino) is available at https: // github. com/ Brazilian-Institute-of-robotics/ offroad_ dataset, and the experiments at https: // github. com/ Brazilian-Institute-of-robotics/ autonomous_ perception . © 2020, CC BY-NC-ND.

关键词： Dust

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Topological Approximate Dynamic programming under Temporal Logic Constraints

Topological Approximate Dynamic Programming under Temporal L...

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IEEE Annual Conference on Decision and Control

作者： Lening Li Jie Fu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper, we develop a model-free approximate dynamic programming method for stochastic systems modeled as Markov decision processes to maximize the probability of satisfying high-level system specifications expressed in a subclass of temporal logic formulas-syntactically co-safe linear temporal logic. Our proposed method includes two steps: First, we decompose the planning problem into a sequence of sub-problems based on the topological property of the task automaton which is translated from a temporal logic formula. Second, we extend a model-free approximate dynamic programming method to solve value functions, one for each state in the task automaton, in an order reverse to the causal dependency. Particularly, we show that the run-time of the proposed algorithm does not grow exponentially with the size of specifications. The correctness and efficiency of the algorithm are demonstrated using a robotic motion planning example.

关键词： Task analysis Planning Dynamic programming Markov processes Automata Approximation algorithms Function approximation

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Redesigning dispenser component to enhance performance crop-dusting agriculture drones

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Materials Today: Proceedings 2023年 81卷 166-172页

作者： Rajvin Subramaniam Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

Agricultural drones are Unmanned Aerial Vehicles (UAVs) applied to farming in order to help increase crop production and improve farm efficiency. Crop-dusting drones are able to spray powdered fertilizer, pesticide, or insecticide on crops from the air, resulting in reduced costs and increased efficiency compared to traditional methods. The objective of this research work was to improve and enhance the performance of the crop-dusting process using agricultural drones. This research paper details the development of an improved storage container design and stabilized release mechanism for crop-dusting. To stabilise the release of payload when the drone is moving or swaying in the air, a gimbal mechanism was added to the release mechanism in which weights were used to produce constant downward force which balances the mechanism. Tests conducted on the prototype system showed that it performs as required. The stabilization tests show an improvement in accuracy of granule spread when using the stabilized release mechanism developed in this research work. As such, the wastage of fertilizers and pesticides can be reduced. The prototype system was also found to much more capable at carrying heavier loads for a longer period of time compared to current designs. Thus, using the prototype system developed for this research work, the size of storage container as well as the weight of fertilizer or pesticide granule payloads can be increased.

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Autonomous fertilizer mixer through the Internet of Things (IoT)

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Materials Today: Proceedings 2023年 81卷 295-301页

作者： Abdul Hadi Ishak Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

Organic fertilizers have been used since the dawn of mankind to improve soil fertility and promote crop growth. However, the process of preparing organic fertilizers for use in agricultural activities is a labour-intensive and time-consuming procedure. Thus, in order to reduce labour costs and improve the efficiency of organic fertilizer preparation, automated organic fertilizer mixers were developed. For this research work, an improved organic fertilizer mixer based on the Internet of Things (IoT) technology capable of monitoring the status of fertilizer production remotely and providing updates and alerts to workers was developed. The aim of this work was to further reduce labour workload and costs, thus increasing efficiency of the organic fertilizer mixing process. In order to accommodate the IoT monitoring system, several modifications and design changes were carried out on the prototype of an automated organic fertilizer mixer. Tests conducted on the prototype unit showed that it performs as required. The IoT monitoring system successfully captures and uploads information on the weight of agricultural waste mixture in the storage drum to the IoT platform. This information can then be used to alert workers on when to add more agricultural waste material to the mixture or remove the processed organic fertilizer. Furthermore, an overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems. Future work includes further automation to the agricultural waste filling and organic fertilizer removal process.

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Design and development of a robot for spraying fertilizers and pesticides for agriculture

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Materials Today: Proceedings 2023年 81卷 242-248页

作者： Afif Shazwan Abdul Ghafar Sami Salama Hussen Hajjaj Kisheen Rao Gsangaya Mohamed Thariq Hameed Sultan Mohd Fazly Mail Lee Seng Hua Department of Mechanical Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Centre for Advanced Mechatronics and Robotics (CaMaRo) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Darul Ehsan Malaysia Laboratory of Biocomposite Technology Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Department of Aerospace Engineering Faculty of Engineering Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia Aerospace Malaysia Innovation Centre (944741-A) Prime Minister’s Department MIGHT Partnership Hub Jalan Impact 63000 Cyberjaya Selangor Darul Ehsan Malaysia Farm Mechanization Program Engineering Research Centre Malaysian Agricultural Research and Development Institute (MARDI) 43400 Serdang Selangor Darul Ehsan Malaysia Laboratory of Biopolymer and Derivatives Institute of Tropical Forestry and Forest Products (INTROP) Universiti Putra Malaysia 43400 UPM Serdang Selangor Darul Ehsan Malaysia

The agriculture industry is one that is highly resource- and labour-intensive. As such, farmers are increasingly turning to technology and automation to address this issue. However, agricultural robots are far too complicated, slow, and costly to be made publicly available. As a result, the agriculture sector still lags behind in integrating modern technologies. This research paper details the development of a low-cost agricultural robot for spraying fertilizers and pesticides in agriculture fields as well as for general crop monitoring. The prototype system is a two-wheeled robot that consists of a mobile base, a spraying mechanism, a wireless controller for controlling the robot movement, and a camera for crop health and growth monitoring as well as detecting the presence of pests in the agriculture field. Tests conducted on the prototype system show that while the productivity of the robot in terms of crop coverage is slightly lower than a human worker, the labour cost savings afforded by the agricultural robot prototype is much greater as it functions completely in an autonomous mode and only requires the operator to control the robot when placing it at the start of the crop path. Furthermore, the prototype system also provides greater resource savings and reduction in the contamination of underground water sources due to leeching process, thus achieving precision agriculture goals. Lastly, the excellent battery life of the prototype system ensures that there will be no increase in the operation times and reduction in the efficiency of the fertilizer and pesticide spraying process due to the recharging times when replacing human workers. Future recommendations include making the agricultural robot fully autonomous, using either a rail- or line-following system, to further reduce the labour requirements and costs.

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Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

Compositional planning in Markov decision processes: Tempora...

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American Control Conference

作者： Xuan Liu Jie Fu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (纸本)9781538679012;9781538679265

In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications.

关键词： Markov chain time scales Planning Formal logic Policies Abstraction Temporal logic accuracy class

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