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arXiv 2024年

作者： Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George the Robotics and AI Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A∗ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

STAGE: Scalable and Traversability-Aware Graph based Explora...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Akash Patel Mario A. V. Saucedo Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

关键词： Legged locomotion Uncertainty Laser radar Navigation Robot vision systems Memory management Cameras

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On Designing Consistent Covariance Recovery from a Deep Learning Visual Odometry Engine

arXiv

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arXiv 2024年

作者： Nir, Jagatpreet Singh Giaya, Dennis Singh, Hanumant NU Field Robotics lab Department of Electrical and Computer Engineering Northeastern University Boston United States

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for these methods remains a challenge. Traditional sensor fusion approaches in a Bayesian framework are well-established, but deep learning techniques with millions of parameters lack efficient methods for uncertainty estimation. This paper addresses the issue of uncertainty estimation for pre-trained deep-learning models in monocular visual odometry. We propose formulating a factor graph on an implicit layer of the deep learning network to recover relative covariance estimates, which allows us to determine the covariance of the visual odometry (VO) solution. We showcase the consistency of the deep learning engine’s covariance approximation with an empirical analysis of the covariance model on the EUROC datasets to demonstrate the correctness of our formulation. © 2024, CC BY.

关键词： Large datasets

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Electro-Pneumatic Interface Framework for PAM-based Humanoid Robot Motion Control with EMG

Electro-Pneumatic Interface Framework for PAM-based Humanoid...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Nathan Hiruy Vidya Sumathy Jakub Haluska George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

In this work, a real-time electro-pneumatic interface framework for emulating human muscular motion on a pneumatic-actuated muscle-based robot is presented. Pneumatic-actuated muscles are flexible actuators that contract and expand using pressure input and are widely used in medical and robotic research to generate motions that mimic human muscles. Electromyographic signals that measure the nerve stimulation of the muscles in an organism, can be used to emulate real-time muscular motion in Pneumatic Artificial Muscle based robots. Here, the proposed electro-pneumatic interface framework takes a real-time electromyographic signal as input and outputs corresponding pneumatic signals for actuating the pneumatic artificial muscles. The electromyographic sensors, attached to the human body, pick up the signals during motion, and the software electro-pneumatic interface computes the required differential pressure for the robot’s motion. In this approach, experimental data is used to model the transfer functions between variables such as the electromyographic signal, robot deflection angle, and differential pressure for the pneumatic artificial muscles. The proposed framework is experimentally validated on a humanoid robot made of pneumatic artificial muscles. The simulation and experiment results show the virtues of the proposed methodology.

关键词： Artificial muscles Humanoid robots Pneumatic systems Muscles Robot sensing systems Electromyography Real-time systems

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BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization

BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detecti...

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Mediterranean Conference on Control and Automation (MED)

作者： Mario A.V. Saucedo Nikolaos Stathoulopoulos Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes $BOX3D$ , a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. $BOX3D$ is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments.

关键词： Location awareness Point cloud compression Three-dimensional displays Laser radar Urban areas Merging Object detection

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Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers

arXiv

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arXiv 2024年

作者： Seisa, Achilleas Santi Satpute, Sumeet Gajanan Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper proposes a novel approach to address the challenges of deploying complex robotic software in large-scale systems, i.e., Centralized Nonlinear Model Predictive Controllers (CNMPCs) for multi-agent systems. The proposed approach is based on a Kubernetes-based scheduling mechanism designed to monitor and optimize the operation of CNMPCs, while addressing the scalability limitation of centralized control schemes. By leveraging a cluster in a real-time cloud environment, the proposed mechanism effectively offloads the computational burden of CNMPCs. Through experiments, we have demonstrated the effectiveness and performance of our system, especially in scenarios where the number of robots is subject to change. Our work contributes to the advancement of cloud-based control strategies and lays the foundation for enhanced performance in cloud-controlled robotic systems. © 2024, CC BY.

关键词： Multi agent systems

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Voxel Map to Occupancy Map Conversion Using Free Space Projection for Efficient Map Representation for Aerial and Ground Robots

arXiv

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arXiv 2024年

作者： Fredriksson, Scott Saradagi, Akshit Nikolakopoulos, George The Robotics and AI group The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps for both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The generated 2D maps can be used for more efficient global navigation for both UAVs and UGVs, in enabling algorithms developed for 2D maps to be useful in 3D applications, and allowing for faster transfer of maps between multiple agents in bandwidth-limited scenarios. The proposed method uses the free space representation in the UFOMap mapping solution to generate 2D occupancy maps. During the 3D to 2D map conversion, the method conducts safety checks and eliminates free spaces in the map with dimensions (in the height axis) lower than the robot’s safety margins. This ensures that an aerial or ground robot can navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts the height of navigable free space and a local estimate of the slope of the floor from the 3D voxel map. The height data is utilized in converting paths generated using the 2D map into paths in 3D space for both UAVs and UGVs. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The effectiveness of the proposed method in enabling computationally efficient navigation for both aerial and ground robots is validated in two different environments, over both static maps and in online implementation in an exploration mission. The methods proposed within this article have been implemented in the popular robotics framework ROS and are open-sourced. The code is available at: https://***/LTURAI/Map-Conversion-3D-Voxel-Map-to-2D-Occupancy-Map. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Lightweight Design and Property Analysis of Humanoid Robot Thigh Integrated Structure with Appearance 16th

Lightweight Design and Property Analysis of Humanoid Robot T...

引用

16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Nie, Daming Gu, Jason Zhang, Yu Jiang, Hongjian Research Center for Intelligent Robotics Zhejiang Lab Hangzhou310058 China Department of Electrical and Computer Engineering Dalhousie University HalifaxB3M 1A2 Canada

ISBN: (纸本)9789819964949

The lightweight and high stiffness leg structure of humanoid robots can effectively reduce rotational inertia and energy consumption, improving the robot's ability to quickly switch motion states. Based on the lightweight requirements of robots, this article proposes an innovative design method for humanoid robot legs based on the "structure-appearance" integration of light-alloy additive manufacturing technology. The load-bearing part of the leg structure is designed as a "shell + density-variable lattice" configuration, and the appearance part is set as a thin shell with equal thickness. The load-bearing part and the appearance one are combined together, resulting in a 20% weight reduction compared to traditional legs. The leg structure sample was processed using metal additive manufacturing method, and tested by a set of personalized fixture. When applying 200 Nm loading under pure torsion conditions, the local strain did not exceed 0.0175%, indicating that it meets the usage requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Anthropomorphic robots

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Robotic Exploration through Semantic Topometric Mapping

Robotic Exploration through Semantic Topometric Mapping

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IEEE International Conference on robotics and Automation (ICRA)

作者： Scott Fredriksson Akshit Saradagi George Nikolakopoulos Department of Computer Science Robotics and AI Group Electrical and Space Engineering at Luleå University of Technology Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method’s online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time.

关键词： Measurement Laser radar Navigation Semantics Skeleton Iron Indoor environment

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Body-aware Local Navigation for Asymmetric Holonomic Robots using Control Barrier Functions

Body-aware Local Navigation for Asymmetric Holonomic Robots ...

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European Control Conference (ECC)

作者： Akshit Saradagi Scott Fredriksson Anton Koval George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this article, we propose a body-aware local navigation strategy for asymmetric holonomic robots for collision-free navigation in narrow pathways with sharp turns. In such scenarios, a robot with non-circular or asymmetric footprint that is comparable to the dimension of the pathways collides with walls when tracking Voronoi paths or risk-aware paths. This problem is addressed in this article through a novel multi-control barrier functions (CBF) based control strategy that achieves the objective of safe collision-free maneuvering at sharp turns. The proposed method is significantly computationally light in comparison to approaches based on model predictive control and online occupancy-grid based free-space and collision detection. In the proposed approach, a minimal set of parameters that characterize a sharp turn and the robot footprint are used to define six control barrier functions that define safe and unsafe regions of operation for a robot. A quadratic programming based CBF safety filter is designed that takes a nominal goal-reaching control as input and returns a minimally-deviating output that enforces the control barrier constraints and renders the safe set forward invariant throughout the turning maneuver. The three kinematic control inputs of the holonomic robot are shared in a conflict-free manner among the six control barrier constraints. The proposed local navigation approach was thoroughly validated in multiple scenarios in a simulated environment, where a robot with asymmetric footprint achieves collision-free maneuvering along multiple sharp turns, while respecting the safety and actuation constraints.

关键词： Laser radar Navigation Kinematics Turning Safety Quadratic programming Mobile robots

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