348 research outputs found

    Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

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    The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

    An Efficient Dynamic Multi-Sources To Single-Destination (DMS-SD) Algorithm In Smart City Navigation Using Adjacent Matrix

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    Dijkstra's algorithm is one of the most popular classic path planning algorithms, achieving optimal solutions across a wide range of challenging tasks. However, it only calculates the shortest distance from one vertex to another, which is hard to directly apply to the Dynamic Multi-Sources to Single-Destination (DMS-SD) problem. This paper proposes a modified Dijkstra algorithm to address the DMS-SD problem, where the destination can be dynamically changed. Our method deploys the concept of Adjacent Matrix from Floyd's algorithm and achieves the goal with mathematical calculations. We formally show that all-pairs shortest distance information in Floyd's algorithm is not required in our algorithm. Extensive experiments verify the scalability and optimality of the proposed method.Comment: International Conference On Human-Centered Cognitive Systems (HCCS) 202

    Multi-Robot Systems: Challenges, Trends and Applications

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    This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

    Geoinformatics in Citizen Science

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    The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

    Performance Evaluation of Pathfinding Algorithms

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    Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls

    The collaborative iterative search approach to multi agent path finding

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    PhD ThesisThis thesis presents a new approach to obtaining optimal and complete solutions to Multi Agent Path Finding (MAPF) problems called Collaborative Iterative Search (CIS). CIS employs a conflict based scheme inspired by the Conflict Based Search (CBS) algorithm and extends this to include a linear order lower level search. The structure of Planar Graphs is leveraged, permitting further optimization of the algorithm. This takes the form of reasoning-based culling of the search space, while maintaining optimality and completeness. Benchmarks provided demonstrate significant performance gains over the existing state of the art, particularly in the case of sparsely populated maps. The thesis draws to a conclusion with a summary of proposed future work

    Risk-Aware Planning for Sensor Data Collection

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    With the emergence of low-cost unmanned air vehicles, civilian and military organizations are quickly identifying new applications for affordable, large-scale collectives to support and augment human efforts via sensor data collection. In order to be viable, these collectives must be resilient to the risk and uncertainty of operating in real-world environments. Previous work in multi-agent planning has avoided planning for the loss of agents in environments with risk. In contrast, this dissertation presents a problem formulation that includes the risk of losing agents, the effect of those losses on the mission being executed, and provides anticipatory planning algorithms that consider risk. We conduct a thorough analysis of the effects of risk on path-based planning, motivating new solution methods. We then use hierarchical clustering to generate risk-aware plans for a variable number of agents, outperforming traditional planning methods. Next, we provide a mechanism for distributed negotiation of stable plans, utilizing coalitional game theory to provide cost allocation methods that we prove to be fair and stable. Centralized planning with redundancy is then explored, planning for parallel task completion to mitigate risk and provide further increased expected value. Finally, we explore the role of cost uncertainty as additional source of risk, using bi-objective optimization to generate sets of alternative plans. We demonstrate the capability of our algorithms on randomly generated problem instances, showing an improvement over traditional multi-agent planning methods as high as 500% on very large problem instances

    An out-of-core method for GPU image mapping on large 3D scenarios of the real world

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    [Abstract] Image mapping on 3D huge scenarios of the real world is one of the most fundamental and computational expensive processes for the integration of multi-source sensing data. Recent studies focused on the observation and characterization of Earth have been enhanced by the proliferation of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few methods have been developed to characterize the study area by fusing heterogeneous data such as thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts required for image mapping on dense and complex geometric models. In this paper, we propose an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed method is out-of-core and it uses available resources of the GPU’s machine to perform two main tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and two sets of multispectral images collected by two drone flights. We focus on launching the proposed method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The results demonstrate the method’s capabilities in terms of performance and versatility to be computed by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded and edge computing devices for 3D image mapping on large-scale scenarios in near real-time.This work has been partially supported through the research projects TIN2017-84968-R, PID2019-104184RB-I00 funded by MCIN/AEI/10.13039/501100011033 and ERDF funds “A way of doing Europe”, as well as by ED431C 2021/30, ED431F 2021/11 funded by Xunta de Galicia and 1381202 by Junta de AndalucíaXunta de Galicia; ED431C 2021/30Xunta de Galicia; ED431F 2021/11Junta de Andalucía; 138120
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