Publications

@article{Petsanis2024,

title = {LFG: An easy-to-use realistic synthetic LandFill Generator},

author = {Th. Petsanis and A. Ch. Kapoutsis and E. B. Kosmatopoulos},

url = {https://www.sciencedirect.com/science/article/pii/S2352711024003066

https://github.com/AthanasiosPetsanis/LFG},

doi = {10.1016/j.softx.2024.101936},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

journal = {SoftwareX},

volume = {28},

abstract = {In this paper we present a Blender add-on named LFG that allows for easy, large and realistic, 3D model LandFill Generation. Large datasets of vast, diverse synthetic landfills are hard to come by, and greatly in need for the purposes of developing and evaluating a multitude of algorithms (e.g. waste classification, 3D-reconstruction, volume estimation algorithms) in the context of research against environment crime. Additionally, they can be used alongside UAV simulators for the development of path-planning algorithms. Although there are some 3D models of landfills available on online 3D-model marketplaces, these are often expensive, low-quality, low-variety and unalterable models. LFG offers customizable, expandable options and realistic features tailored for landfill generation and research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Korkas2024,

title = {Distributed and Multi-Agent Reinforcement Learning Framework for Optimal Electric Vehicle Charging Scheduling},

author = {Ch. D. Korkas and Ch. D. Tsaknakis and A. Ch. Kapoutsis and E. B. Kosmatopoulos},

url = {https://www.mdpi.com/1996-1073/17/15/3694},

doi = {doi.org/10.3390/en17153694},

year  = {2024},

date = {2024-07-26},

urldate = {2024-07-26},

journal = {Energies},

volume = {17},

number = {15},

issue = {15},

pages = {3694},

abstract = {The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Stefanopoulou2024,

title = {Improving time and energy efficiency in multi-UAV coverage operations by optimizing the UAVs’ initial positions},

author = {A. Stefanopoulou and E. K.Raptis and S. D. Apostolidis and S. Gkelios and A. Ch. Kapoutsis and S. A. Chatzichristofis and S. Vrochidis and E. B. Kosmatopoulos},

url = {https://link.springer.com/article/10.1007/s41315-024-00333-2

https://www.researchsquare.com/article/rs-3671974/v1

https://github.com/alice-st/DARP_Optimal_Initial_Positions

https://github.com/emmarapt/RealWorld2AirSim-DARP},

doi = {https://doi.org/10.1007/s41315-024-00333-2},

year  = {2024},

date = {2024-04-22},

urldate = {2024-04-01},

journal = {International Journal of Intelligent Robotics and Applications},

abstract = {This paper focuses on Coverage Path Planning (CPP) methodologies, particularly in the context of multi-robot missions, to efficiently cover user-defined Regions of Interest (ROIs) using groups of UAVs, while emphasizing on the reduction of energy consumption and mission duration. Optimizing the efficiency of multi-robot CPP missions involves addressing critical factors such as path length, the number of turns, re-visitations, and launch positions. Achieving these goals, particularly in complex and concave ROIs with No-Go Zones, is a challenging task. This work introduces a novel approach to address these challenges, emphasizing the selection of launch points for UAVs. By optimizing launch points, the mission’s energy and time efficiency are significantly enhanced, leading to more efficient coverage of the selected ROIs. To further support our research and foster further exploration on this topic, we provide the open-source implementation of our algorithm and our evaluation mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Krestenitis2024,

title = {Overcome the Fear Of Missing Out: Active sensing UAV scanning for precision agriculture},

author = {M. Krestenitis and E. K.Raptis and A. Ch. Kapoutsis and K. Ioannidis and E. B. Kosmatopoulos and S. Vrochidis},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0921889023002208

https://arxiv.org/abs/2312.09730

https://zenodo.org/records/10202929

https://github.com/emmarapt/OverFOMO

},

doi = {https://doi.org/10.1016/j.robot.2023.104581},

year  = {2024},

date = {2024-02-01},

urldate = {2024-02-01},

journal = {Robotics and Autonomous Systems},

volume = {172},

pages = {104581},

abstract = {This paper deals with the problem of informative path planning for a UAV deployed for precision agriculture applications. First, we observe that the “fear of missing out” data lead to uniform, conservative scanning policies over the whole agricultural field. Consequently, employing a non-uniform scanning approach can mitigate the expenditure of time in areas with minimal or negligible real value, while ensuring heightened precision in information-dense regions. Turning to the available informative path planning methodologies, we discern that certain methods entail intensive computational requirements, while others necessitate training on an ideal world simulator. To address the aforementioned issues, we propose an active sensing coverage path planning approach, named OverFOMO, that regulates the speed of the UAV in accordance with both the relative quantity of the identified classes, i.e. crops and weeds, and the confidence level of such detections. To identify these instances, a robust Deep Learning segmentation model is deployed. The computational needs of the proposed algorithm are independent of the size of the agricultural field, rendering its applicability on modern UAVs quite straightforward. The proposed algorithm was evaluated with a simu-realistic pipeline, combining data from real UAV missions and the high-fidelity dynamics of AirSim simulator, showcasing its performance improvements over the established state of affairs for this type of missions. An open-source implementation of the algorithm and the evaluation pipeline is also available: https://github.com/emmarapt/OverFOMO.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Decomposing User-Defined Tasks in a Reinforcement Learning setup using TextWorld},

author = {Th. Petsanis and Ch. Keroglou and A. Ch. Kapoutsis and E. B. Kosmatopoulos and G. Ch. Sirakoulis},

url = {https://www.frontiersin.org/articles/10.3389/frobt.2023.1280578/full

https://github.com/AthanasiosPetsanis/DiplomaClone

https://github.com/AthanasiosPetsanis/DiplomaClone/blob/main/Main.ipynb},

doi = {10.3389/frobt.2023.1280578},

year  = {2023},

date = {2023-12-22},

urldate = {2023-12-22},

journal = {Frontiers in Robotics and AI},

volume = {10},

pages = {1280578},

abstract = {The current paper proposes a Hierarchical Reinforcement Learning (HRL) method to decompose a complex task into simpler sub-tasks and leverage those to improve training of an autonomous agent in a simulated environment. For practical reasons (i.e., illustrating purposes, easy implementation, user friendly interface, useful functionalities), we employ two python frameworks called TextWorld and MiniGrid. MiniGrid functions as a 2D simulated representation of the real environment while TextWorld functions as a high-level abstraction of this simulated environment.Training on this abstraction disentangles manipulation from navigation actions and allows us to design a dense reward function instead of a sparse reward function for the lower-level environment which, as we show, improves performance of training. Formal methods are utilized throughout the paper to establish that our algorithm is not prevented to derive to solutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kapoutsis2023,

title = {ACRE: Actor-Critic with Reward-Preserving Exploration},

author = {A. Ch. Kapoutsis and D. I. Koutras and Ch. D. Korkas and E. B. Kosmatopoulos},

url = {https://link.springer.com/article/10.1007/s00521-023-08845-x

https://github.com/athakapo/ACRE

https://paperswithcode.com/paper/acre-actor-critic-with-reward-preserving

https://www.youtube.com/watch?v=epBwZb5kpTc},

doi = {https://doi.org/10.1007/s00521-023-08845-x},

year  = {2023},

date = {2023-08-14},

urldate = {2023-08-14},

journal = {Neural Computing and Applications},

abstract = {While reinforcement learning (RL) algorithms have generated impressive strategies for a wide range of tasks, the performance improvements in continuous-domain, real-world problems do not follow the same trend. Poor exploration and quick convergence to locally optimal solutions play a dominant role. Advanced RL algorithms attempt to mitigate this issue by introducing exploration signals during the training procedure. This successful integration has paved the way to introduce signals from the intrinsic exploration branch. ACRE algorithm is a framework that concretely describes the conditions for such an integration, avoiding transforming the Markov decision process into time varying, and as a result, making the whole optimization scheme brittle and susceptible to instability. The key distinction of ACRE lies in the way of handling and storing both extrinsic and intrinsic rewards. ACRE is an off-policy, actor-critic style RL algorithm that separately approximates the forward novelty return. ACRE is shipped with a Gaussian mixture model to calculate the instantaneous novelty; however, different options could also be integrated. Using such an effective early exploration, ACRE results in substantial improvements over alternative RL methods, in a range of continuous control RL environments, such as learning from policy-misleading reward signals. Open-source implementation is available here: https://github.com/athakapo/ACRE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Apostolidis2023,

title = {Systematically Improving the Efficiency of Grid-Based Coverage Path Planning Methodologies in Real-World UAVs' Operations},

author = {S. D. Apostolidis and G. Vougiatzis and A. Ch. Kapoutsis and S. A. Chatzichristofis and E. B. Kosmatopoulos},

url = {https://www.mdpi.com/2504-446X/7/6/399},

doi = {https://dx.doi.org/10.3390/drones7060399},

year  = {2023},

date = {2023-06-15},

urldate = {2023-06-15},

journal = {Drones},

abstract = { This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region's representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs' coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method's effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{K.Raptis2023b,

title = {CoFly: An automated, AI-based open-source platform for UAV precision agriculture applications},

author = {E. K.Raptis and K. Egglezos and O. Kypris and M. Krestenitis and A. Ch. Kapoutsis and K. Ioannidis and S. Vrochidis and E. B. Kosmatopoulos},

url = {https://www.sciencedirect.com/science/article/pii/S2352711023001103

https://github.com/CoFly-Project/cofly-gui},

doi = {https://doi.org/10.1016/j.softx.2023.101414},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {SoftwareX},

abstract = {This paper presents a modular and holistic Precision Agriculture platform, named CoFly, incorporating custom-developed AI and ICT technologies with pioneering functionalities in a UAV-agnostic system. Cognitional operations of micro Flying vehicles are utilized for data acquisition incorporating advanced coverage path planning and obstacle avoidance functionalities. Photogrammetric outcomes are extracted by processing UAV data into 2D fields and crop health maps, enabling the extraction of high-level semantic information about seed yields and quality. Based on vegetation health, CoFly incorporates a pixel-wise processing pipeline to detect and classify crop health deterioration sources. On top of that, a novel UAV mission planning scheme is employed to enable site-specific treatment by providing an automated solution for a targeted, on-the-spot, inspection. Upon the acquired inspection footage, a weed detection module is deployed, utilizing deep-learning methods, enabling weed classification. All of these capabilities are integrated inside a cost-effective and user-friendly end-to-end platform functioning on mobile devices. CoFly was tested and validated with extensive experimentation in agricultural fields with lucerne and wheat crops in Chalkidiki, Greece showcasing its performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{K.Raptis2023c,

title = {End-to-end Precision Agriculture UAV-Based Functionalities Tailored to Field Characteristics},

author = {E. K.Raptis and M. Krestenitis and K. Egglezos and O. Kypris and K. Ioannidis and L. Doitsidis and A. Ch. Kapoutsis and S. Vrochidis and I. Kompatsiaris and E. B. Kosmatopoulos},

url = {https://link.springer.com/article/10.1007/s10846-022-01761-7

https://www.youtube.com/watch?v=C0hdCu-ZRQk},

doi = {https://doi.org/10.1007/s10846-022-01761-7},

year  = {2023},

date = {2023-01-27},

urldate = {2023-01-27},

journal = {Journal of Intelligent & Robotic Systems volume },

volume = {107},

number = {23},

issue = {2},

abstract = {This paper presents a novel, low-cost, user-friendly Precision Agriculture platform that attempts to alleviate the drawbacks of limited battery life by carefully designing missions tailored to each field’s specific, time-changing characteristics. The proposed system is capable of designing coverage missions for any type of UAV, integrating field characteristics into the resulting trajectory, such as irregular field shape and obstacles. The collected images are automatically processed to create detailed orthomosaics of the field and extract the corresponding vegetation indices. A novel mechanism is then introduced that automatically extracts possible problematic areas of the field and subsequently designs a follow-up UAV mission to acquire extra information on these regions. The toolchain is finished by using a deep learning module that was made just for finding weeds in the close-examination flight. For the development of such a deep-learning module, a new weed dataset from the UAV’s perspective, which is publicly available for download, was collected and annotated. All the above functionalities are enclosed in an open-source, end-to-end platform, named Cognitional Operations of micro Flying vehicles (CoFly). The effectiveness of the proposed system was tested and validated with extensive experimentation in agricultural fields with cotton in Larissa, Greece during two different crop sessions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Krestenitis2022b,

title = {CoFly-WeedDB: A UAV image dataset for weed detection and species identification},

author = {M. Krestenitis and E. K.Raptis and A. Ch. Kapoutsis and K. Ioannidis and E. B. Kosmatopoulos and S. Vrochidis and I. Kompatsiaris},

url = {https://www.sciencedirect.com/science/article/pii/S235234092200782X

https://github.com/CoFly-Project/CoFly-WeedDB

https://zenodo.org/record/6697343#.YrQpwHhByV4},

doi = {https://doi.org/10.1016/j.dib.2022.108575},

issn = {2352-3409},

year  = {2022},

date = {2022-09-05},

urldate = {2022-09-05},

journal = {Data in Brief},

volume = {45},

pages = {108575},

abstract = {The CoFly-WeedDB contains 201 RGB images (∼436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The 1280×720 RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karatzinis2022,

title = {Coordinating heterogeneous mobile sensing platforms for effectively monitoring a dispersed gas plume},

author = {G. D. Karatzinis and P. Michailidis and I. T. Michailidis and A. Ch. Kapoutsis and E. B. Kosmatopoulos and Y. S. Boutalis},

url = {https://www.researchgate.net/publication/362883988_Coordinating_heterogeneous_mobile_sensing_platforms_for_effectively_monitoring_a_dispersed_gas_plume

https://github.com/athakapo/A-distributed-plug-n-play-algorithm-for-multi-robot-applications},

doi = {http://dx.doi.org/10.3233/ICA-220690},

year  = {2022},

date = {2022-08-19},

urldate = {2022-08-19},

journal = { Integrated Computer-Aided Engineering},

abstract = {In order to sufficiently protect active personnel and physical environment from hazardous leaks, recent industrial practices integrate innovative multi-modalities so as to maximize response efficiency. Since the early detection of such incidents portrays the most critical factor for providing efficient response measures, the continuous and reliable surveying of industrial spaces is of primary importance. Current study develops a surveying mechanism, utilizing a swarm of heterogeneous aerial mobile sensory platforms, for the continuous monitoring and detection of CH4 dispersed gas plumes. In order to timely represent the CH4 diffusion progression incident, the research concerns a simulated indoor, geometrically complex environment, where early detection and timely response are critical. The primary aim was to evaluate the efficiency of a novel multi-agent, closed-loop, algorithm responsible for the UAV path-planning of the swarm, in comparison with an efficient a state-of-the-art path-planning EGO methodology, acting as a benchmark. Abbreviated as Block Coordinate Descent Cognitive Adaptive Optimization (BCD-CAO) the novel algorithm outperformed the Efficient Global Optimization (EGO) algorithm, in seven simulation scenarios, demonstrating improved dynamic adaptation of the aerial UAV swarm towards its heterogeneous operational capabilities. The evaluation results presented herein, exhibit the efficiency of the proposed algorithm for continuously conforming the mobile sensing platforms’ formation towards maximizing the total measured density of the diffused volume plume.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Apostolidis2022b,

title = {Cooperative Multi-UAV Coverage Mission Planning Platform for Remote Sensing Applications},

author = {S. D. Apostolidis and P. Ch. Kapoutsis and A. Ch. Kapoutsis and E. B. Kosmatopoulos},

editor = {Gaurav Sukhatme},

url = {https://link.springer.com/article/10.1007/s10514-021-10028-3

https://arxiv.org/abs/2201.07030

http://choosepath.ddns.net/

https://kapoutsis.info/wp-content/uploads/2022/02/mCPP-flyer.pdf

https://github.com/savvas-ap/mCPP-optimized-DARP

https://github.com/savvas-ap/cpp-simulated-evaluations

https://paperswithcode.com/paper/cooperative-multi-uav-coverage-mission

https://paperswithcode.com/dataset/cpp-simulated-evaluation

https://www.youtube.com/watch?v=JQrqt1dS4A8},

doi = {10.1007/s10514-021-10028-3},

year  = {2022},

date = {2022-00-15},

urldate = {2022-00-15},

journal = {Autonomous Robots},

abstract = {This paper proposes a novel mission planning platform, capable of efficiently deploying a team of UAVs to cover complex-shaped areas, in various remote sensing applications. Under the hood lies a novel optimization scheme for grid-based methods, utilizing Simulated Annealing algorithm, that significantly increases the achieved percentage of coverage and improves the qualitative features of the generated paths. Extensive simulated evaluation in comparison with a state-of-the-art alternative methodology, for coverage path planning (CPP) operations, establishes the performance gains in terms of achieved coverage and overall duration of the generated missions. On top of that, DARP algorithm is employed to allocate sub-tasks to each member of the swarm, taking into account each UAV's sensing and operational capabilities, their initial positions and any no-fly-zones possibly defined inside the operational area. This feature is of paramount importance in real-life applications, as it has the potential to achieve tremendous performance improvements in terms of time demanded to complete a mission, while at the same time it unlocks a wide new range of applications, that was previously not feasible due to the limited battery life of UAVs. In order to investigate the actual efficiency gains that are introduced by the multi-UAV utilization, a simulated study is performed as well. All of these capabilities are packed inside an end-to-end platform that eases the utilization of UAVs' swarms in remote sensing applications. Its versatility is demonstrated via two different real-life applications: (i) a photogrametry for precision agriculture and (ii) an indicative search and rescue for first responders missions, that were performed utilizing a swarm of commercial UAVs. An implementation of the mCPP methodology introduced in this work, as well as a link for a demonstrative video and a link for a fully functional, on-line hosted instance of the presented platform can be found here: https://github.com/savvas-ap/mCPP-optimized-DARP.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Koutras2021,

title = {MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments},

author = {D. I. Koutras and A. Ch. Kapoutsis and A. A. Amanatiadis and E. B. Kosmatopoulos},

url = {https://www.mdpi.com/2079-9292/10/22/2751/

https://arxiv.org/abs/2107.09996

https://github.com/dimikout3/MarsExplorer

https://paperswithcode.com/paper/marsexplorer-exploration-of-unknown-terrains

https://www.azom.com/news.aspx?newsID=57311},

doi = {https://doi.org/10.3390/electronics10222751},

year  = {2021},

date = {2021-11-11},

urldate = {2021-11-11},

journal = {Electronics},

volume = {10},

number = {22},

pages = {2751},

publisher = {mdpi},

abstract = {This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot's dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by evaluating PPO learned policy algorithm side-by-side with frontier-based exploration strategies. A study on the performance curves revealed that PPO-based policy was capable of performing adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit areas uncovered, underlying the capability of RL-based methodologies to tackle exploration tasks efficiently. The source code can be found at: https://github.com/dimikout3/MarsExplorer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kapoutsis2021,

title = {Building synergetic consensus for dynamic gas-plume tracking applications using UAV platforms},

author = {A. Ch. Kapoutsis and I. T. Michailidis and Y. Boutalis and E. B. Kosmatopoulos},

url = {https://www.sciencedirect.com/science/article/pii/S0045790621000525

https://kapoutsis.info/wp-content/uploads/2021/03/Building-Synergetic-Consensus-for-Dynamic-Gas-Plume-Tracking-Applications-using-UAV-Platforms.pdf

https://www.youtube.com/watch?v=9jKD6ORLxgQ&feature=youtu.be

https://www.youtube.com/watch?v=sJeSuCd8ciw

https://github.com/athakapo/A-distributed-plug-n-play-algorithm-for-multi-robot-applications},

doi = {10.1016/j.compeleceng.2021.107029},

year  = {2021},

date = {2021-03-09},

urldate = {2021-03-09},

journal = {Computers & Electrical Engineering},

volume = {91},

abstract = {This article investigates the problem of deploying a swarm of UAVs equipped with gas sensors for industrial remote gas-plume sensing. This setup’s objective is to continuously adjust the swarm formation to maximize the combined perception for the dynamically evolved plume’s cloud, focusing around areas with the highest concentration/intensity. Initially, such a setup is formulated into an optimization problem, the solution of which could be acquired by the maximization of an appropriately defined objective function. Due to the model-free approach, this objective function’s analytical form is not available, prohibiting standard gradient descent methodologies. To this end, a tracking algorithm is developed and studied, which operates in a distributed manner and enables the UAV swarm to build a common consensus dynamically, during the evolution of the leakage phenomenon. The overall performance is tested in a simulative yet realistic environment using ANSYS Fluent suite, considering a simultaneous gas-leak incident at two different points. Aside from the standalone evaluation study, the proposed gas-plume tracking scheme is able to outperform a state-of-the-art alternative algorithm, namely Efficient Global Optimization (EGO), in various simulation setups, deploying a different number of UAVs on the field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Michailidis2021,

title = {Embedding autonomy in large-scale IoT ecosystems using CAO and L4G-CAO},

author = {I. T. Michailidis and A. Ch. Kapoutsis and Ch. D. Korkas and P. T. Michailidis K. A. Alexandridou and Ch. Ravanis and E. B. Kosmatopoulos},

url = {https://link.springer.com/article/10.1007/s43926-021-00003-w

https://kapoutsis.info/wp-content/uploads/2021/03/Discover_IoT___Springer___Revision.pdf},

doi = {10.1007/s43926-021-00003-w},

year  = {2021},

date = {2021-02-24},

journal = {Discover Internet of Things},

volume = {1},

number = {8},

abstract = {Recently, special attention has been paid in developing methodologies and systems for embedding autonomy within smart devices (Things). Moreover, as Things typically operate in an interconnected IoT ecosystem, autonomous operation must be performed in a cooperative fashion so the different Things coordinate their autonomous actions towards meeting high-level objectives and policies. Embedding Things with cooperative autonomy typically requires a tedious and costly effort not only during the original ecosystem deployment but throughout its lifetime. The current study describes CAO (Cognitive Adaptive Optimization)—and its distributed counterpart L4G-CAO (Local for Global Cognitive Adaptive Optimization)—which can overcome this shortcoming. CAO and L4G-CAO—which have recently been introduced and tested in a variety of IoT applications—can embed Things with cooperative autonomy in a plug-n-play fashion, i.e., without requiring the aforementioned tedious and costly effort. Results of the application of the aforementioned approaches in three different application domains (smart homes and districts, intelligent traffic systems and coordinated swarms of robots) are also presented. The presented results demonstrate the potential, of both approaches, to exploit the IoT automation functionalities in order to significantly improve the overall IoT performance without tedious effort.

},

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pubstate = {published},

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}

@article{koutras2020autonomous,

title = {Autonomous and Cooperative Design of the Monitor Positions for a Team of UAVs to Maximize the Quantity and Quality of Detected Objects},

author = {D. I. Koutras and A. Ch. Kapoutsis and E. B. Kosmatopoulos},

editor = { Jonathan Roberts},

url = {https://ieeexplore.ieee.org/document/9126171

https://arxiv.org/abs/2007.01247

https://paperswithcode.com/paper/autonomous-and-cooperative-design-of-the

https://www.youtube.com/watch?v=L8ycmS20rZs

https://kapoutsis.info/wp-content/uploads/2020/11/IROS_Main_Presentation.pptx

https://github.com/dimikout3/ConvCAOAirSim



},

doi = {10.1109/LRA.2020.3004780},

issn = {2377-3766},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {IEEE Robotics and Automation Letters},

volume = {5},

number = {3},

pages = {4986-4993},

abstract = {This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness. The situational awareness is expressed by the number and quality of unique objects of interest, inside the UAVs’ fields of view. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs’ configuration. Then, a novel navigation algorithm, capable of optimizing the previously defined score, without taking into consideration the dynamics of either UAVs or environment, is proposed. A cornerstone of the proposed approach is that it shares the same convergence characteristics as the block coordinate descent (BCD) family of approaches. The effectiveness and performance of the proposed navigation scheme were evaluated utilizing a series of experiments inside the AirSim simulator. The experimental evaluation indicates that the proposed navigation algorithm was able to consistently navigate the swarm of UAVs to “strategic” monitoring positions and also adapt to the different number of swarm sizes. Source code is available at https://github.com/dimikout3/ConvCAOAirSim.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kapoutsis2019distributed,

title = {A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions},

author = {A. Ch. Kapoutsis and S. A. Chatzichristofis and E. B. Kosmatopoulos},

editor = {John M Hollerbach},

url = {https://journals.sagepub.com/doi/10.1177/0278364919845054

https://arxiv.org/abs/2111.07441

https://github.com/athakapo/pyBCD-CAO

https://github.com/athakapo/A-distributed-plug-n-play-algorithm-for-multi-robot-applications

https://paperswithcode.com/paper/a-distributed-plug-n-play-algorithm-for-multi

https://www.youtube.com/watch?v=HTYfnbRvf5U

},

doi = {https://doi.org/10.1177/0278364919845054},

issn = {1573-0409},

year  = {2019},

date = {2019-05-08},

urldate = {2019-05-08},

journal = {The International Journal of Robotics Research},

volume = {38},

number = {7},

pages = {813-832},

abstract = {This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot’s subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot’s cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kapoutsis2017darp,

title = {DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning},

author = {A. Ch. Kapoutsis and S. A. Chatzichristofis and E. B. Kosmatopoulos},

editor = {Spyros Tzafestas and Kimon P. Valavanis},

url = {https://link.springer.com/article/10.1007/s10846-016-0461-x

https://zenodo.org/records/2591050

https://medium.com/@athanasios.kapoutsis/darp-divide-areas-algorithm-for-optimal-multi-robot-coverage-path-planning-2fed77b990a3

https://www.youtube.com/watch?v=LrGfvma41Ak

https://kapoutsis.info/wp-content/uploads/2021/06/DARP.pptx

https://kapoutsis.info/wp-content/uploads/2017/01/DARP.zip

https://github.com/athakapo/DARP

https://github.com/alice-st/DARP-Python

https://github.com/cpswarm/swarm_functions/tree/kinetic-devel/area_division},

doi = {10.1007/s10846-016-0461-x},

issn = {1573-0409},

year  = {2017},

date = {2017-04-01},

urldate = {2017-04-01},

journal = {Journal of Intelligent & Robotic Systems},

volume = {86},

number = {3},

pages = {663–680},

abstract = {This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an O(n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practical sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage (video demonstration and standalone application are available on-line http://tinyurl.com/DARP-app).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kapoutsis2015real,

title = {Real-Time Adaptive Multi-Robot Exploration with Application to Underwater Map Construction},

author = {A. Ch. Kapoutsis and S. A. Chatzichristofis and L. Doitsidis and J. Sousa and J. Pinto and J. Braga and E. B. Kosmatopoulos},

url = {https://link.springer.com/article/10.1007/s10514-015-9510-8

https://kapoutsis.info/wp-content/uploads/2016/04/noptilus_Final.pdf

https://www.youtube.com/watch?v=menK5tMRw-s

},

doi = {https://doi.org/10.1007/s10514-015-9510-8},

issn = {0929-5593},

year  = {2016},

date = {2016-08-15},

journal = {Autonomous Robots},

volume = {40},

number = {6},

pages = {987–1015},

abstract = {This paper deals with the problem of autonomous exploration of unknown areas using teams of Autonomous X Vehicles (AXVs)—with X standing for Aerial, Underwater or Sea-surface—where the AXVs have to autonomously navigate themselves so as to construct an accurate map of the unknown area. Such a problem can be transformed into a dynamic optimization problem which, however, is NP-complete and thus infeasible to be solved. A usual attempt is to relax this problem by employing greedy (optimal one-step-ahead) solutions which may end-up quite problematic. In this paper, we first show that optimal one-step-ahead exploration schemes that are based on a transformed optimization criterion can lead to highly efficient solutions to the multi-AXV exploration. Such a transformed optimization criterion is constructed using both theoretical analysis and experimental investigations and attempts to minimize the “disturbing” effect of deadlocks and nonlinearities to the overall exploration scheme. As, however, optimal one-step-ahead solutions to the transformed optimization criterion cannot be practically obtained using conventional optimization schemes, the second step in our approach is to combine the use of the transformed optimization criterion with the cognitive adaptive optimization (CAO): CAO is a practicably feasible computational methodology which adaptively provides an accurate approximation of the optimal one-step-ahead solutions. The combination of the transformed optimization criterion with CAO results in a multi-AXV exploration scheme which is both practically implementable and provides with quite efficient solutions as it is shown both by theoretical analysis and, most importantly, by extensive simulation experiments and real-life underwater sea-floor mapping experiments in the Leixes port, Portugal.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{iakovidou2015localizing,

title = {Localizing Global Descriptors for Content Based Image Retrieval},

author = {C. Iakovidou and N. Anagnostopoulos and A. Ch. Kapoutsis and Y. Boutalis and M. Lux and S. A. Chatzichristofis},

url = {https://link.springer.com/article/10.1186/s13634-015-0262-6

},

doi = {https://doi.org/10.1186/s13634-015-0262-6},

year  = {2015},

date = {2015-09-07},

journal = {EURASIP Journal on Advances in Signal Processing},

number = {80},

pages = {1-20},

abstract = {In this paper, we explore, extend and simplify the localization of the description ability of the well-established MPEG-7 (Scalable Colour Descriptor (SCD), Colour Layout Descriptor (CLD) and Edge Histogram Descriptor (EHD)) and MPEG-7-like (Color and Edge Directivity Descriptor (CEDD)) global descriptors, which we call the SIMPLE family of descriptors. Sixteen novel descriptors are introduced that utilize four different sampling strategies for the extraction of image patches to be used as points of interest. Designing with focused attention for content-based image retrieval tasks, we investigate, analyse and propose the preferred process for the definition of the parameters involved (point detection, description, codebook sizes and descriptors’ weighting strategies). The experimental results conducted on four different image collections reveal an astonishing boost in the retrieval performance of the proposed descriptors compared to their performance in their original global form. Furthermore, they manage to outperform common SIFT- and SURF-based approaches while they perform comparably, if not better, against recent state-of-the-art methods that base their success on much more complex data manipulation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}