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On Robust Context-Aware Navigation for Autonomous Ground Vehicles

IEEE robotics AND automation LETTERS 2025年第2期10卷 1449-1456页

作者： Forte, Paolo Gupta, Himanshu Andreasson, Henrik Koeckemann, Uwe Lilienthal, Achim J. Orebro Univ Ctr Appl Autonomous Sensor Syst S-70182 Orebro Sweden Tech Univ Munich Munich Inst Robot & Machine Intelligence D-80992 Munich Germany

We propose a context-aware navigation framework designed to support the navigation of autonomous ground vehicles, including articulated ones. The proposed framework employs a behavior tree with novel nodes to manage the navigation tasks: planner and controller selections, path planning, path following, and recovery. It incorporates a weather detection system and configurable global path planning and controller strategy selectors implemented as behavior tree action nodes. These components are integrated into a sub-tree that supervises and manages available options and parameters for global planners and control strategies by evaluating map and real-time sensor data. The proposed approach offers three key benefits: overcoming the limitations of single planner strategies in challenging scenarios;ensuring efficient path planning by balancing between optimization and computational effort;and achieving smoother navigation by reducing path curvature and improving drivability. The performance of the proposed framework is analyzed empirically, and compared against state of the art navigation systems with single path planning strategies.

关键词： Navigation Path planning Meteorology Robots Robot sensing systems Vehicle dynamics Space vehicles Semantics Planning Servers Autonomous vehicle navigation motion and path planning robotics and automation in construction

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Spatial Resolution Ensured Configuration Planning for Visual Inspection Using Pan-Tilt-Zoom Camera-Equipped Robots

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IEEE ACCESS 2025年 13卷 54105-54114页

作者： Wu, Weitong Haneda, Masaya Funabora, Yuki Doki, Shinji Doki, Kae Nagoya Univ Grad Sch Engn Dept Elect Engn & Comp Sci Nagoya Aichi 4648603 Japan Aichi Inst Technol Dept Elect & Elect Engn Toyota Aichi 4700392 Japan

We propose a novel pan-tilt-zoom (PTZ) camera configuration planning method to improve data collection for structure visual inspections. This method plans a set of configurations for PTZ camera-equipped robots to collect visual data of target surfaces at the required spatial resolution. Unlike previous methods that focus solely on optimizing camera positions to cover different areas of the target, our method fully leverages all the degrees of freedom of PTZ cameras. Our method allows the robot to completely cover targets with far less movement by adjusting the camera direction and zoom level. Experiments on bridge inspections using a PTZ ground inspection platform have validated that our method ensured both the completeness and spatial resolution of the collected data. Furthermore, drone-based inspection simulations have demonstrated that our method avoids unnecessary robot movement and reduces inspection time by up to 38% compared to the previous algorithm, significantly enhancing inspection efficiency.

关键词： Cameras Planning Inspection Robot vision systems Robots Spatial resolution Visualization Three-dimensional displays Heuristic algorithms Bridges Motion and path planning aerial systems: perception and autonomy field robots robotics and automation in construction autonomous agents reactive and sensor-based planning

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Enhancing Robotic Precision in construction: A Modular Factor Graph-Based Framework to Deflection and Backlash Compensation Using High-Accuracy Accelerometers

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IEEE robotics AND automation LETTERS 2025年第1期10卷 288-295页

作者： Kindle, Julien Loetscher, Michael Alessandretti, Andrea Cadena, Cesar Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland Hilti AG Schaan 9494 Liechtenstein

Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash in various parts along the kinematic chain. In this letter, we introduce a novel approach that integrates deflection and backlash compensation models with high-accuracy accelerometers, significantly enhancing position accuracy. Our method employs a modular framework based on a factor graph formulation to estimate the state of the kinematic chain, leveraging acceleration measurements to inform the model. Extensive testing on publicly released datasets, reflecting real-world construction disturbances, demonstrates the advantages of our approach. The proposed method reduces the 95% error threshold in the xy-plane by 50% compared to the state-of-the-art Virtual Joint Method, and by 31% when incorporating base tilt compensation.

关键词： Robots Accuracy Kinematics Accelerometers Robot sensing systems Position measurement Robot kinematics End effectors Measurement uncertainty Calibration robotics and automation in construction localization sensor fusion

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Physics-Aware Combinatorial Assembly Sequence Planning Using Data-Free Action Masking

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IEEE robotics AND automation LETTERS 2025年第5期10卷 4882-4889页

作者： Liu, Ruixuan Chen, Alan Zhao, Weiye Liu, Changliu Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Westlake High Sch Austin TX 78746 USA

Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. This letter studies assembly sequence planning (ASP) for physical combinatorial assembly. Given the shape of the desired object, the goal is to find a sequence of actions for placing unit primitives to build the target object. In particular, we aim to ensure the planned assembly sequence is physically executable. However, ASP for combinatorial assembly is particularly challenging due to its combinatorial nature. To address the challenge, we employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object. Specifically, we design an online physics-aware action mask that filters out invalid actions, which effectively guides policy learning and ensures violation-free deployment. In the end, we apply the proposed method to Lego assembly with more than 250 3D structures. The experiment results demonstrate that the proposed method plans physically valid assembly sequences to build all structures, achieving a 100% success rate, whereas the best comparable baseline fails more than 40 structures.

关键词： Assembly Shape Layout Planning Training Three-dimensional displays Physics Optimization Filters Complexity theory task planning reinforcement learning robotics and automation in construction

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Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation automation

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IEEE robotics AND automation LETTERS 2021年第4期6卷 6297-6304页

作者： Selby, Nicholas S. Asada, H. Harry MIT Sch Engn 77 Massachusetts Ave Cambridge MA 02139 USA

Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e., taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design.

关键词： robotics and automation in construction reinforcement learning

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A Vision-Based Autonomous UAV Inspection Framework for Unknown Tunnel construction Sites With Dynamic Obstacles

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IEEE robotics AND automation LETTERS 2023年第8期8卷 4983-4990页

作者： Xu, Zhefan Chen, Baihan Zhan, Xiaoyang Xiu, Yumeng Suzuki, Christopher Shimada, Kenji Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this letter proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.

关键词： Field robotics motion and path planning perception and autonomy robotics and automation in construction

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SMAC: Symbiotic Multi-Agent construction

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IEEE robotics AND automation LETTERS 2021年第2期6卷 3200-3207页

作者： Wagner, Caleb Dhanaraj, Neel Rizzo, Trevor Contreras, Josue Liang, Hannan Lewin, Gregory Pinciroli, Carlo Worcester Polytech Inst Dept Robot Engn Worcester MA 01609 USA

We present a novel concept of a heterogeneous, distributed platform for autonomous 3D construction. The platform is composed of two types of robots acting in a coordinated and complementary fashion: (i) A collection of communicating smart construction blocks behaving as a form of growable smart matter, and capable of planning and monitoring their own state and the construction progress;and (ii) A team of inchworm-inspired builder robots designed to navigate and modify the 3D structure, following the guidance of the smart blocks. We describe the design of the hardware and introduce algorithms for navigation and construction that support a wide class of 3D structures. We demonstrate the capabilities of our concept and characterize its performance through simulations and real-robot experiments.

关键词： Building automation distributed robot systems multi-robot systems robotics and automation in construction swarm robotics

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PLASTR: Planning for Autonomous Sampling-Based Trowelling

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IEEE robotics AND automation LETTERS 2023年第8期8卷 5069-5076页

作者： Kuhlmann-Jorgensen, Mads A. Pankert, Johannes Pietrasik, Lukasz L. Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland

Plaster is commonly used in the construction industry to finish walls and ceilings, but the application is labor-intensive and physically strenuous, which motivates the need for automation. We present PLASTR, a receding horizon optimization-based planning algorithm for robotic plaster trowelling. It samples trowelling sequence rollouts from a new plaster simulator and weights them according to the flatness of the finished wall. The proposed simulator approximates the real-world plaster-trowel interaction adequately while allowing execution orders of magnitude faster than real-time. We evaluate PLASTR in simulation and on a real-world test setup and compare it to two handcrafted heuristic baseline algorithms. PLASTR performs equal to or better than the best heuristic in terms of material coverage for both simulated and real-world experiments while being 50% more efficient in terms of trowelled distance.

关键词： robotics and automation in construction motion and path planning optimization and optimal control

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Magnetic Wall-Climbing Wheels With Controllable Adhesion Reduction via Soft Magnetic Material

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IEEE robotics AND automation LETTERS 2024年第12期9卷 11553-11560页

作者： Tian, Yang Jitsukawa, Hayato Ma, Shugen Zhang, Guoteng Shinshu Univ Dept Engn Nagano 3808553 Japan Ritsumeikan Univ Dept Robot Kusatsu Shiga 5258577 Japan Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Syst Hub Guangzhou 511453 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

With the aging of critical infrastructure like bridges and plant facilities, the development of innovative wall-climbing robots using permanent magnets has become increasingly important. Traditional designs of such robots depend on controlling the position of lifters or permanent magnets to control the adhesion condition, which introduces significant safety concerns, including inconsistent adhesion during surface transitions and the risk of falls when control is lost. To overcome these issues, this paper introduces a novel magnet wheel design that utilizes Soft Magnetic Material (SMM) to control the reduction of adhesive force in a specific direction. The effect on adhesion was shown with a comparative analysis of various magnet and SMM configurations. Based on the analyses, a wheel design was provided with investigating the effect of the SMM cover region. For verifying the effectiveness of the adhesion reduction, a robot with the proposed wheel is presented and analyzed to realize wall-climbing tasks. In experiments, a prototype robot equipped with the proposed wheel design demonstrates enhanced safety for wall-climbing tasks under controlled conditions.

关键词： Adhesives Magnetic flux Soft magnetic materials Magnetic shielding Magnetic noise Magnetic circuits Robots Wheels Force Mobile robots Field robots climbing robots robot safety wheeled robots robotics and automation in construction

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DUFOMap: Efficient Dynamic Awareness Mapping

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IEEE robotics AND automation LETTERS 2024年第6期9卷 5038-5045页

作者： Duberg, Daniel Zhang, Qingwen Jia, Mingkai Jensfelt, Patric KTH Royal Inst Technol Div Robot Percept & Learning RPL Stockholm 114 28 Sweden Hong Kong Univ Sci & Technol Robot Inst Hong Kong 999077 Peoples R China

The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the world to support, for example, localization and planning. Current solutions are often applied in post-processing, where parameter tuning allows the user to adjust the setting for a specific dataset. In this letter, we propose DUFOMap, a novel dynamic awareness mapping framework designed for efficient online processing. Despite having the same parameter settings for all scenarios, it performs better or is on par with state-of-the-art methods. Ray casting is utilized to identify and classify fully observed empty regions. Since these regions have been observed empty, it follows that anything inside them at another time must be dynamic. Evaluation is carried out in various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. DUFOMap outperforms the state of the art in terms of accuracy and computational efficiency.

关键词： Mapping object detection segmentation and categorization robotics and automation in construction

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