====== Annotated Bibliography Template ====== **Author:** Blake Hament Email: blakehament@gmail.com \\ **Date:** Last modified on 04/20/2017 \\ **Keywords:** UGV, UAV, Localization, Navigation, Visual Servoing, Heterogeneous Robot Cooperation \\ ===== Papers ===== Zip file containing all papers coming soon... {{:passprotected.zip|Password Protected Papers Zip File}} ===== Annotated References ===== ==== 1. Air-Ground Localization and Map Augmentation Using Monocular Dense Reconstruction ==== [[http://ieeexplore.ieee.org/document/6696924/|Air-Ground Localization and Map Augmentation Using Monocular Dense Reconstruction]]\\ Publisher: IROS 2013\\ Keywords (platform, field of research, algorithm or approach/methodologies, more details ): image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays \\ **Bibtex:**\\ @INPROCEEDINGS{6696924, author={C. Forster and M. Pizzoli and D. Scaramuzza}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Air-ground localization and map augmentation using monocular dense reconstruction}, year={2013}, pages={3971-3978}, keywords={image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays}, doi={10.1109/IROS.2013.6696924}, ISSN={2153-0858}, month={Nov},} **This paper describes:** An algorithm for combining scans from a UAV and UGV\\ **The authors present (simulations, experiments, theory):** Algo for combining maps from a UGV and UAV: egomotion estimation (SLAM on UAV and UGV respectively); Dense Reconstruction (compute multiple depth maps from UAV data, use cost function to fuse); Global localization (align UGV and UAV maps with cost function and Zero Mean Sum of Squared Differences); Pose Refinement (Iterative Closest Point) \\ **From this presentation, the paper concludes that:** The algo presented is superior to existing methods because it field tests indicate much faster runtime (ms)\\ **From the state-of-the-art, the paper identifies challenges in:** Issues in past like algo’s only working for flat planes, processing time being too slow \\ **The paper addresses these challenges by:** adding filters and regularization\\ **The results of this approach are:** Faster processing times during localization and mapping\\ **The paper presents the following theoretical principles:** (1) Simultaneous UAV/UGV SLAM;(2) Dense Reconstruction; (3) Monte Carlo Global Localization; (4) Pose Refinement\\ **The principles are explained (choose: well/fairly/poorly):** fairly \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Figure 6 **show the (choose: correct/questionable) application of the principles:** correct \\ But the costmap/Monte-Carlo based global alignment in Figure 8 was difficult to interpret **From the principles and results, the paper concludes:** (1) the proposed method allows fusion of maps captured from very different perspectives; (2) this was demonstrated to be valuable because the UGV's map of a 3D structure was significantly enhanced by fusion with UAV map **Blake liked this paper because:** \\ - It provided a full overview of the authors' localization and mapping pipeline. - The authors gave a very clear description of their Dense Reconstruction algo that helped me better understand the principles behind it. - The authors employ excellent graphs to help the reader visualize trajectory errors and translation errors from point cloud operations. **I disliked this paper because:** I had some trouble identifying what was being represented in some of the figures due to small image sizes and less-than-helpful captions ; \\ **I would have liked to see** Much more detail on the Monte-Carlo global localization \\ **Three things I learned from this paper were:** \\ - Digital Surface Models - Dense Reconstruction - Iterative Closest Point algo\\ **Time reading and annotating:** ~ 1.5 hours --- ==== 2. COOPERATIVE GROUND AND AIR SURVEILLANCE ==== **[[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1678135&isnumber=35300|COOPERATIVE GROUND AND AIR SURVEILLANCE]]**\\ Publisher: ICRA 2006\\ Keywords (platform, field of research, algorithm or approach/methodologies, more details ): aerospace robotics;aircraft;cooperative systems;decentralised control;mobile robots;remotely operated vehicles;sensors;surveillance;telerobotics;air surveillance;cooperative system;decentralized control;ground surveillance;onboard sensors;unmanned aerial vehicles;unmanned ground vehicles;Cameras;Land vehicles;Object detection;Robot kinematics;Robot sensing systems;Robot vision systems;Robotics and automation;Surveillance;Uncertainty;Unmanned aerial vehicles \\ ** Bibtex:**\\ @ARTICLE{1678135, author={B. Grocholsky and J. Keller and V. Kumar and G. Pappas}, journal={IEEE Robotics Automation Magazine}, title={Cooperative air and ground surveillance}, year={2006}, volume={13}, number={3}, pages={16-25}, keywords={aerospace robotics;aircraft;cooperative systems;decentralised control;mobile robots;remotely operated vehicles;sensors;surveillance;telerobotics;air surveillance;cooperative system;decentralized control;ground surveillance;onboard sensors;unmanned aerial vehicles;unmanned ground vehicles;Cameras;Land vehicles;Object detection;Robot kinematics;Robot sensing systems;Robot vision systems;Robotics and automation;Surveillance;Uncertainty;Unmanned aerial vehicles}, doi={10.1109/MRA.2006.1678135}, ISSN={1070-9932}, month={Sept},} **This paper describes:** Active sensing with a team of heterogeneous robots for target localization and collaborative mapping\\ **The authors present (simulations, experiments, theory):**target location estimation equation based on uncertainty of measurements \\ **From this presentation, the paper concludes that:** robot teams can more efficiently search and scan an area by using the measurement uncertainties to “information surf” -- picking trajectories based on following the highest information gain gradient \\ **From the state-of-the-art, the paper identifies challenges in:** real-time processing of localizing, navigation, and mapping due to high dimensionality of data\\ **The paper addresses these challenges by:** Building on decentralized estimation algorithms from linear dynamic models with assumptions of Guassian noise; active sensor network (ASN); certainty grids \\ **The results of this approach are:** the benefits of this approach include active sensing, decentralized processing, measurement trajectories that are more efficient and take advantage of the inherent strengths and weaknesses of each robot platform \\ **The paper presents the following theoretical principles:** - Information Gradients/Surfing - Active Sensor Network - Gaussian Noise in point cloud **The principles are explained (choose: well/fairly/poorly):** fairly \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Figure 8. **show the (choose: correct/questionable) application of the principles:** correct (great representation of the iso-mutual information contours the robot experiences during "information surfing" \\ **From the principles and results, the paper concludes:** - This method allows control of heterogenous robots without tailoring to the robots specific capabilities. - Every robot has it's own certainty grid containing computed possibilities of target detection at various position. - This certainty grid changes constantly as new information is introduced - This approach is easily scalable \\ **Blake liked this paper because:** - Excellent figures - Information surfing is a very interesting concept that I am excited to apply - I enjoyed their treatment of probabilities\\ **I disliked this paper because:** No complaints, great paper\\ **I would have liked to see** All of the work was done in 2D with height map projections to capture 3D of the environment. It would be interesting to see this work applied to a complex 3D environment like a building in which 2D points in the ground plane can map to multiple heights.\\ **Three things I learned from this paper were:** \\ - Information Surfing - Control of heterogenous robots, independent of specific capabilities of the specific robots - Reactive Controllers **Time reading and annotating:** ~ 1.5 hours --- ==== 3. ISSUES IN COOPERATIVE AIR/GROUND ROBOTIC SYSTEMS ==== **[[https://pdfs.semanticscholar.org/243f/1f012cdac91cc1cf177543042909c04d027c.pdf|ISSUES IN COOPERATIVE AIR/GROUND ROBOTIC SYSTEMS]]**\\ Publisher: Springer Tracts in Advanced Robotics 2010\\ Keywords (platform, field of research, algorithm or approach/methodologies, more details ): UAV, UGV, Cooperation, Taxonomy \\ ** Bibtex:**\\ @Inbook{Lacroix2011, author="Lacroix, Simon and Le Besnerais, Guy", editor="Kaneko, Makoto and Nakamura, Yoshihiko", title="Issues in Cooperative Air/Ground Robotic Systems", bookTitle="Robotics Research: The 13th International Symposium ISRR", year="2011", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="421--432", isbn="978-3-642-14743-2", doi="10.1007/978-3-642-14743-2_35", url="http://dx.doi.org/10.1007/978-3-642-14743-2_35" } **This paper describes:** Cooperation and perception schemas for ground-aerial robot teams \\ **The authors present (simulations, experiments, theory):** a taxonomy of UGV-UAV search/mapping schemes\\ **From this presentation, the paper concludes that:** The authors suggest that current G-A mapping techniques can be improved by associating data according to geometric primitives present in the environment \\ **From the state-of-the-art, the paper identifies challenges in:** stitching together images or whole maps from UGV and UAV\\ **The paper addresses these challenges by:** suggesting the addition of models to help estimation like models for the motion of the robots and the geometry of targets of interest \\ **The results of this approach are:** G-A robot teams can cooperate in 3 different scenarios (either G or A is the supporter of the other robot, or they are ~equal cooperators); in perceiving, must solve the problems of data registration and fusion \\ **The paper presents the following theoretical principles:** - Localization - Spin-images - Traversability models - Gemetric models - Navigation supports\\ **The principles are explained (choose: well/fairly/poorly):** well \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Fig. 5 **show the (choose: correct/questionable) application of the principles:** correct \\ **From the principles and results, the paper concludes:** - The key problem w/ UGV-UAV cooperation is data registration and fusion - Time constraints are salient in designing the algo's - Use prior knowledge like motion projections and geometric properties to improve data fusion \\ **Blake liked this paper because:** - Solid overview of UGV-UAV search chemes - Good figures\\ **I disliked this paper because:** information was not very in-depth \\ **I would have liked to see** a taxonomy that extends beyond search/ mapping applications \\ **Three things I learned from this paper were:** \\ - Transversability Models - Navigation Support - UGV-UAV cooperation schemes\\ **Time reading and annotating:** ~ 1 hour ---\\ ==== 4. PLANNING FOR A GROUND-AIR ROBOTIC SYSTEM WITH COLLABORATIVE LOCALIZATION ==== **[[http://ieeexplore.ieee.org/document/7487146/|PLANNING FOR A GROUND-AIR ROBOTIC SYSTEM WITH COLLABORATIVE LOCALIZATION]]**\\ Publisher: ICRA 2016\\ Keywords (platform, field of research, algorithm or approach/methodologies, more details ): autonomous aerial vehicles;PAD planner;SLC planner;UGV-UAV team operating indoors;collaborative localization;controller-based motion primitives;ground-air robotic system;high-quality localization information;payload capacity;planning adaptive dimensionality;robust navigation capabilities;state lattice planner;unmanned aerial vehicles;unmanned ground vehicles;visual features;Collaboration;Lattices;Planning;Robot sensing systems;Trajectory \\ ** Bibtex:**\\ @INPROCEEDINGS{7487146, author={J. Butzke and K. Gochev and B. Holden and E. J. Jung and M. Likhachev}, booktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)}, title={Planning for a ground-air robotic system with collaborative localization}, year={2016}, pages={284-291}, keywords={autonomous aerial vehicles;PAD planner;SLC planner;UGV-UAV team operating indoors;collaborative localization;controller-based motion primitives;ground-air robotic system;high-quality localization information;payload capacity;planning adaptive dimensionality;robust navigation capabilities;state lattice planner;unmanned aerial vehicles;unmanned ground vehicles;visual features;Collaboration;Lattices;Planning;Robot sensing systems;Trajectory}, doi={10.1109/ICRA.2016.7487146} **This paper describes:** A state lattice planner using controller-based motion primitives (SLC) that uses planning with adaptive dimensionality (PAD) \\ **The authors present (simulations, experiments, theory):** experiments using a state lattice planner using controller-based motion primitives (SLC) that uses planning with adaptive dimensionality (PAD) \\ **From this presentation, the paper concludes that:** success rates and trial stats showing the SLC PAD approach has a high rate of success and quick processing times\\ **From the state-of-the-art, the paper identifies challenges in:** real-time planning for heterogeneous robot teams in which the robots do not need to travel in formation \\ **The paper addresses these challenges by:** This paper proposes PAD such that only the relevant dimensions at a given point are considered. Ex: When moving a piano inside from the street, planning orientation is irrelevant in the drive-way but essential once you get to the doorway. \\ **The results of this approach are:** superior success rates and times vs. existing methods \\ **The paper presents the following theoretical principles:** - State Lattice Controller (SLC) - Motion Primitives - Planning with Adaptive Dimensionality (PAD) \\ **The principles are explained (choose: well/fairly/poorly):** fairly \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Table 1 **show the (choose: correct/questionable) application of the principles:** correct, but many of the figures are not very educational or interesting \\ **From the principles and results, the paper concludes:** - Existing methods of localization for UGV-UAV teams are lacking because of slow processing times and poor assumptions made by planners - PAD allows for much faster processing times \\ **Blake liked this paper because:** (fill-in-the-blank with at least 3 reasons if possible).\\ **I disliked this paper because:** the authors didn't provide a toolkit with their algo's ;) \\ **I would have liked to see** the same work done without assuming any prior knowledge of the map \\ **Three things I learned from this paper were:** \\ - state-based planning - planning with adaptive dimensionality - controlling with motion primitives **Time reading and annotating:** ~ 2 hours --- \\ ==== 5. A Tutorial on Visual Servo Control ==== **[[http://www.cs.jhu.edu/~hager/Public/Publications/TutorialTRA96.pdf|A Tutorial on Visual Servo Control]]**\\ **Publisher:** ITRA 1996 \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** Jacobian matrices;correlation methods;feature extraction;feedback;image representation;motion control;optical tracking;robot dynamics;robot vision;servomechanisms;computer vision;coordinate transformations;correlation-based methods;feedback;image Jacobian;image feature tracking;image formation process;image-based systems;position-based system;robotic manipulators;tutorial;velocity representation;visual servo control;Control systems;Costs;Manipulators;Manufacturing;Robot control;Robot sensing systems;Robot vision systems;Servosystems;Tutorial;Visual servoing \\ **Bibtex:**\\ @ARTICLE{538972, author={S. Hutchinson and G. D. Hager and P. I. Corke}, journal={IEEE Transactions on Robotics and Automation}, title={A tutorial on visual servo control}, year={1996}, volume={12}, number={5}, pages={651-670}, keywords={Jacobian matrices;correlation methods;feature extraction;feedback;image representation;motion control;optical tracking;robot dynamics;robot vision;servomechanisms;computer vision;coordinate transformations;correlation-based methods;feedback;image Jacobian;image feature tracking;image formation process;image-based systems;position-based system;robotic manipulators;tutorial;velocity representation;visual servo control;Control systems;Costs;Manipulators;Manufacturing;Robot control;Robot sensing systems;Robot vision systems;Servosystems;Tutorial;Visual servoing}, doi={10.1109/70.538972}, ISSN={1042-296X}, month={Oct},} \\ **This paper describes:** A taxonomy of and instructions for visual servoing (VS)\\ **The authors present (simulations, experiments, theory):** A text tutorial containing the equations that govern the various approaches to VS\\ **From this presentation, the paper concludes that:** A disclaimer that the paper presents a fundamental introduction, and readers should follow up with cited papers relevant to the VS they seek to implement.\\ **From the state-of-the-art, the paper identifies challenges in:** processing times and environments with low visibility\\ **The paper addresses these challenges by:** image-based rather than position-based VS; better camera positioning\\ **The results of this approach are:** referenced in the 80+ citations in which they were implemented\\ **The paper presents the following theoretical principles:** - End point open vs. closed loop control - Position vs. image based control - Dynamic look-and-move vs. direct visual servo \\ **The principles are explained (choose: well/fairly/poorly):** well \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Figs 3-6 **show the (choose: correct/questionable) application of the principles:** correct and very clear comparison of various VS architectures \\ **From the principles and results, the paper concludes:** - If the system is tracking movement of a target with known movement in cartesian coordinates, position-based tracking makes sense - Otherwise, image-based tracking works better because it can be done independently of errors in robot kinematics or camera calibration \\ **Blake liked this paper because:** - It described relevant image convolution techniques - It gave a concise but broad overview of the VS field - It helped me refine the VS architecture most suited to UGV-UAV docking \\ **I disliked this paper because:** I had to stop very frequently to look up computer vision or controls vocabulary; \\ **I would have liked to see** more intermediary steps articulated \\ **Three things I learned from this paper were:** \\ - Taxonomy of VS methods - Several specific image matrix operations that are essential to VS - Image-based VS tends to be more accurate, especially for the types of applications I will be implementing \\ **Time reading and annotating:** ~ 5 hours --- \\ ==== 6. A visual servoing docking approach for marsupial robotic system==== **[[http://ieeexplore.ieee.org.ezproxy.library.unlv.edu/document/6896395/|A visual servoing docking approach for marsupial robotic system]]**\\ **Publisher:** IEEE 2014 \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** cameras;feedback;mobile robots;multi-robot systems;robot vision;virtual machines;visual servoing;adistance state;aiming state;angular camera;around state;atangent state;blind state;child robot;decision-making unit;docking heading orientation;docking motion guide;image feature feedback;image-infor virtual machine;marsupial robotic system;parking state;pose refresher;rotational DOF;simulation platform design;task modelling;transform conditions;vertical V-shaped visual benchmark design;visual servoing docking approach;Benchmark testing;Cameras;Decision making;Robot kinematics;Robot vision systems;Virtual machining;Marsupial Robotic system;docking;image-infor virtual machine;visual servoing \\ **Bibtex:**\\ @INPROCEEDINGS{6896395, author={P. Zhao and Z. Cao and L. Xu and C. Zhou and D. Xu}, booktitle={Proceedings of the 33rd Chinese Control Conference}, title={A visual servoing docking approach for marsupial robotic system}, year={2014}, pages={8321-8325}, keywords={cameras;feedback;mobile robots;multi-robot systems;robot vision;virtual machines;visual servoing;adistance state;aiming state;angular camera;around state;atangent state;blind state;child robot;decision-making unit;docking heading orientation;docking motion guide;image feature feedback;image-infor virtual machine;marsupial robotic system;parking state;pose refresher;rotational DOF;simulation platform design;task modelling;transform conditions;vertical V-shaped visual benchmark design;visual servoing docking approach;Benchmark testing;Cameras;Decision making;Robot kinematics;Robot vision systems;Virtual machining;Marsupial Robotic system;docking;image-infor virtual machine;visual servoing}, doi={10.1109/ChiCC.2014.6896395}, month={July},} \\ **This paper describes:** A docking method for a child robot loading into a compartment in a mother robot\\ **The authors present (simulations, experiments, theory):** task model for docking, algo, and simulated results \\ **From this presentation, the paper concludes that:** the simulated results confirm the validity of the docking approach and it's anti-interrupt ability\\ **From the state-of-the-art, the paper identifies challenges in:** Controls for and implementation of marsupial robots\\ **The paper addresses these challenges by:** applying standard color and geometry triggered VS controls to docking of marsupial robots\\ **The results of this approach are:** Quick docking times, robust performance even with unexpected perturbations in the robots motion/rotation from the researchers\\ **The paper presents the following theoretical principles:** - Marsupial robotics - Visual Servoing - Decision-making in Different States \\ **The principles are explained (choose: well/fairly/poorly):** well \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** Fig. 4 **show the (choose: correct/questionable) application of the principles:**correct application of the decision-making algo \\ **From the principles and results, the paper concludes:** - Future work will focus on retrieval of child robots \\ **Blake liked this paper because:** - Almost identical VS docking strategy to my method for UGV docking in a box suspended from a UAV - They showed their rotation and transformation matrices which will be a good check for my future work if I pursue the box method \\ **I disliked this paper because:** Although I am happy for the walkthrough of their project as I might compare my results at various stages, I am not sure they pushed any borders of knowledge ; \\ **I would have liked to see** More discussion of the simulation \\ **Three things I learned from this paper were:** \\ - Vocabulary for the stages in docking - "V" approach to docking with VS - Pose refreshment in simulation-- could apply similar equations to VR \\ **Time reading and annotating:** ~ 15 min --- \\ ==== 7. Multi-rotor drone tutorial: systems, mechanics, control and state estimation ==== **[[https://link.springer.com/article/10.1007/s11370-017-0224-y|Multi-rotor drone tutorial: systems, mechanics, control and state estimation]]**\\ **Publisher:** Intelligent Service Robotics 2017 \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** Components · Control · Modeling · Multi-rotor drone · Sensor fusion \\ **Bibtex:**\\ @Article{Yang2017, author="Yang, Hyunsoo and Lee, Yongseok and Jeon, Sang-Yun and Lee, Dongjun", title="Multi-rotor drone tutorial: systems, mechanics, control and state estimation", journal="Intelligent Service Robotics", year="2017", volume="10", number="2", pages="79--93", issn="1861-2784", doi="10.1007/s11370-017-0224-y", url="http://dx.doi.org/10.1007/s11370-017-0224-y" } \\ **This paper describes:** \\ **The authors present (simulations, experiments, theory):** \\ **From this presentation, the paper concludes that:** \\ **From the state-of-the-art, the paper identifies challenges in:** \\ **The paper addresses these challenges by:** \\ **The results of this approach are:** \\ **The paper presents the following theoretical principles:** - Ordered List Item - \\ **The principles are explained (choose: well/fairly/poorly):** \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** **show the (choose: correct/questionable) application of the principles:** \\ **From the principles and results, the paper concludes:** - \\ **Blake liked this paper because:** - 1 - 2 - 3 \\ **I disliked this paper because:** ; \\ **I would have liked to see** \\ **Three things I learned from this paper were:** \\ - 1 - 2 - 3 \\ **Time reading and annotating:** ~ hours --- \\ --- \\ ==== . ==== **[[|]]**\\ **Publisher:** \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** \\ **Bibtex:**\\ \\ **This paper describes:** \\ **The authors present (simulations, experiments, theory):** \\ **From this presentation, the paper concludes that:** \\ **From the state-of-the-art, the paper identifies challenges in:** \\ **The paper addresses these challenges by:** \\ **The results of this approach are:** \\ **The paper presents the following theoretical principles:** - Ordered List Item - \\ **The principles are explained (choose: well/fairly/poorly):** \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** **show the (choose: correct/questionable) application of the principles:** \\ **From the principles and results, the paper concludes:** - \\ **Blake liked this paper because:** - 1 - 2 - 3 \\ **I disliked this paper because:** ; \\ **I would have liked to see** \\ **Three things I learned from this paper were:** \\ - 1 - 2 - 3 \\ **Time reading and annotating:** ~ hours --- \\ --- \\ ==== . ==== **[[|]]**\\ **Publisher:** \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** \\ **Bibtex:**\\ \\ **This paper describes:** \\ **The authors present (simulations, experiments, theory):** \\ **From this presentation, the paper concludes that:** \\ **From the state-of-the-art, the paper identifies challenges in:** \\ **The paper addresses these challenges by:** \\ **The results of this approach are:** \\ **The paper presents the following theoretical principles:** - Ordered List Item - \\ **The principles are explained (choose: well/fairly/poorly):** \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** **show the (choose: correct/questionable) application of the principles:** \\ **From the principles and results, the paper concludes:** - \\ **Blake liked this paper because:** - 1 - 2 - 3 \\ **I disliked this paper because:** ; \\ **I would have liked to see** \\ **Three things I learned from this paper were:** \\ - 1 - 2 - 3 \\ **Time reading and annotating:** ~ hours --- ==== . ==== **[[|]]**\\ **Publisher:** \\ **Keywords (platform, field of research, algorithm or approach/methodologies, more details ):** \\ **Bibtex:**\\ \\ **This paper describes:** \\ **The authors present (simulations, experiments, theory):** \\ **From this presentation, the paper concludes that:** \\ **From the state-of-the-art, the paper identifies challenges in:** \\ **The paper addresses these challenges by:** \\ **The results of this approach are:** \\ **The paper presents the following theoretical principles:** - Ordered List Item - \\ **The principles are explained (choose: well/fairly/poorly):** \\ **For example (fill-in-the-blank e.g. the equations, graphs, figures),:** **show the (choose: correct/questionable) application of the principles:** \\ **From the principles and results, the paper concludes:** - \\ **Blake liked this paper because:** - 1 - 2 - 3 \\ **I disliked this paper because:** ; \\ **I would have liked to see** \\ **Three things I learned from this paper were:** \\ - 1 - 2 - 3 \\ **Time reading and annotating:** ~ hours --- \\ --- \\