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Found 14 papers, 1 papers with code
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Our contributions include (1) a reinforcement learning framework for UAV trajectory planning that dynamically integrates multi-objective considerations, (2) an analysis of human perceptions towards gendered and anthropomorphized drones in SAR contexts, and (3) the application of similarity-based experience replay for enhanced learning efficiency in complex SAR scenarios.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
Learning High-level Semantic-Relational Concepts for SLAM
Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as Planes and Rooms, whose relationship is mathematically defined.
Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption
This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO).
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Faster Optimization in S-Graphs Exploiting Hierarchy
A global optimization of the compressed graph is performed every time a loop closure is detected.
Adaptive Super-Twisting Controller Design for Accurate Trajectory Tracking Performance of Unmanned Aerial Vehicles
In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances.
On SORA for High-Risk UAV Operations under New EU Regulations: Perspectives for Automated Approach
Accordingly, we propose an alternative workflow, considering the same steps, while addressing the challenges and pitfalls, to shorten the whole process.
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph).
S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations
In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them.
Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution.
Visual SLAM: What are the Current Trends and What to Expect?
In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation.
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol.
Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments.
From SLAM to Situational Awareness: Challenges and Survey
The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation.
A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles
Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees.
Robotics Optimization and Control
