Robotics and Autonomous Systems最新文献_第2页

Deep reinforcement learning-based design with observation buffer of rolling motion for snake-like robots 基于深度强化学习的蛇形机器人滚动运动观察缓冲器设计

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.robot.2026.105370

Akio Yamano, Satomi Suzuki, Tsuyoshi Kimoto, Takashi Iwasa

Snake-like robots can travel over environments that are difficult for wheeled mobility mechanisms. However, undulating locomotion requires high power consumption. We propose an efficient method that integrates the center-of-gravity (COG) shifting for the navigation of the robot to address the aforementioned problem. In the proposed method, the snake-like robot transforms into a tire-like shape to realize a parallel two-wheeled configuration. Subsequently, by deforming the head or tail sections, the position of the COG is changed, and the resulting gravitational torque generates a rolling motion. The proposed method allows the use of rolling motion with high traveling efficiency on level ground and undulating locomotion in water as well as other uneven surfaces. The rolling motion designed in previous research was achieved by the feedback of the direction of gravity measured by an acceleration sensor. Therefore, it was only designed to be capable of traveling on smooth floors or asphalt, making it difficult to maintain straight traveling when road conditions change. This paper presents a controller design method using deep reinforcement learning (RL) to achieve robust traveling by the rolling motion. We conducted experiments using the controller designed by RL and compared the experimental results with numerical simulations. Experiments demonstrated that the RL-designed rolling motion achieved higher straightness than that of previous methods and higher traveling efficiency than conventional undulating locomotion.

蛇形机器人可以在轮式移动机构难以移动的环境中行走。然而，波动运动需要高功耗。为了解决上述问题，我们提出了一种集成重心漂移的机器人导航方法。在提出的方法中，将蛇形机器人转化为类似轮胎的形状，实现平行的两轮构型。随后，通过变形头部或尾部部分，改变COG的位置，由此产生的重力扭矩产生滚动运动。所提出的方法允许在平地上使用具有高行进效率的滚动运动和在水中以及其他不平坦表面上使用波动运动。先前研究设计的滚动运动是通过加速度传感器测量重力方向的反馈来实现的。因此，它只被设计成能够在光滑的地面或沥青上行驶，这使得它很难在路况变化时保持直线行驶。提出了一种利用深度强化学习（RL）实现滚动运动鲁棒运动的控制器设计方法。利用RL设计的控制器进行了实验，并将实验结果与数值模拟结果进行了比较。实验表明，rl设计的滚动运动比以往的方法具有更高的直线度，比传统的波动运动具有更高的行驶效率。

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引用次数: 0

Integrated trajectory planning and tracking control for FWID-AV collision avoidance with multi-actuator control distribution 基于多致动器控制分布的fwidd - av避碰综合轨迹规划与跟踪控制

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-11 DOI: 10.1016/j.robot.2026.105355

Zezhao Wang , Conggan Ma , Yi Li , Ruiyi Wu

As a major development direction of modern automobiles, autonomous vehicles can significantly improve driving safety, efficiency, and economic benefits, making them an urgent societal need. However, existing reports often overlook the impact of prediction inaccuracies in obstacle motion, particularly in complex traffic scenarios. In addition, local trajectory planning and tracking are typically treated as separate tasks, which may lead to poor tracking accuracy of the local reference trajectory and even controller failure. With that in mind, this paper focuses on four-wheel-independent-drive autonomous vehicles (FWID-AV) and proposes an integrated control framework that considers local trajectory planning and trajectory tracking for dynamic obstacle avoidance based on Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) algorithms. Firstly, the vehicle dynamics model is established considering the specific functional requirements of AV trajectory planning and tracking control. Then, a new planning-integrated controller is developed for FWID-AV to achieve collision avoidance. Finally, the MATLAB/Simulink-CarSim joint simulation platform is built, and the effectiveness of the proposed integrated control framework is verified through simulation and experimental tests, which enhances the performance of collision avoidance, trajectory tracking, handling stability, and real-time performance for vehicle.

自动驾驶汽车作为现代汽车的重要发展方向，可以显著提高驾驶安全性、效率和经济效益，成为社会迫切需要。然而，现有的报告往往忽略了预测不准确对障碍物运动的影响，特别是在复杂的交通场景中。此外，局部轨迹规划和跟踪通常被视为单独的任务，这可能导致局部参考轨迹的跟踪精度较差，甚至控制器失效。基于此，本文以四轮独立驾驶自动驾驶汽车为研究对象，提出了一种基于模型预测控制（MPC）和粒子群优化（PSO）算法的综合控制框架，该框架兼顾了局部轨迹规划和轨迹跟踪的动态避障控制。首先，根据自动驾驶汽车轨迹规划和跟踪控制的具体功能需求，建立了车辆动力学模型；在此基础上，设计了一种新型规划集成控制器，实现了自动驾驶汽车的避碰功能。最后，搭建了MATLAB/Simulink-CarSim联合仿真平台，通过仿真和实验验证了所提综合控制框架的有效性，提高了车辆的避碰性能、轨迹跟踪性能、操纵稳定性和实时性。

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引用次数: 0

CMAE-Traj: A contrastive masked autoencoder framework for trajectory prediction CMAE-Traj：一种用于弹道预测的对比掩码自编码器框架

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-02-10 DOI: 10.1016/j.robot.2026.105391

Yamei Xu , Hong Zhang , Zhenghan Gao , Chengming Liu , Zhe Zhao

Predicting the future trajectories of surrounding agents is crucial for the safe operation of autonomous vehicles. Self-supervised learning has gained widespread attention in trajectory prediction, as it alleviates the high cost of annotating trajectory data by pretraining on unlabeled data. However, due to limitations in capturing spatio-temporal interactions and decoupling short-term dynamics and long-term trends, current self-supervised learning methods struggle to model complex behaviors between agents. To overcome these challenges, we propose a novel masked autoencoder with contrastive learning for trajectory prediction that effectively extracts the complicated interactions of agents in the driving environment. Specifically, we introduce a temporal-social mask module that captures short-term motion patterns by jointly modeling temporal dynamics and social interactions of agents, enabling the model to gain a comprehensive understanding from multiple perspectives. Moreover, we introduce a contrastive mask alignment strategy that learns consistent long-term motion trends by treating features from the same trajectory as positive samples and features from different trajectories as negative samples. Extensive experiments on the Argoverse 2, INTERACTION and Waymo Open Motion datasets demonstrate that our model significantly outperforms previous self-supervised learning approaches, achieving competitive results in trajectory prediction.

预测周围智能体的未来轨迹对于自动驾驶汽车的安全运行至关重要。自监督学习在轨迹预测中得到了广泛的关注，因为它通过对未标记的数据进行预训练，减轻了对轨迹数据进行标注的高昂成本。然而，由于在捕捉时空相互作用和解耦短期动态和长期趋势方面的局限性，目前的自监督学习方法难以对智能体之间的复杂行为进行建模。为了克服这些挑战，我们提出了一种新的具有对比学习的掩蔽自编码器，用于轨迹预测，有效地提取驾驶环境中智能体的复杂相互作用。具体而言，我们引入了一个时间-社会掩模模块，该模块通过联合建模agent的时间动态和社会互动来捕获短期运动模式，使模型能够从多个角度获得全面的理解。此外，我们引入了一种对比掩模对齐策略，该策略通过将来自相同轨迹的特征作为正样本，将来自不同轨迹的特征作为负样本来学习一致的长期运动趋势。在Argoverse 2、INTERACTION和Waymo Open Motion数据集上进行的大量实验表明，我们的模型显著优于以前的自监督学习方法，在轨迹预测方面取得了具有竞争力的结果。

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引用次数: 0

A half-cooperative strategy based Deep Q-network algorithm for multi-agent dynamic target hunting 基于半合作策略的深度q网络多智能体动态目标搜索算法

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-24 DOI: 10.1016/j.robot.2026.105368

Xiaoyan Wang, Xin Yu, Xi Fang

In fields like rescue, military, and law enforcement, effective target hunting is vital. Current algorithms for dynamic target hunting often fail in multi-target scenarios due to unknown environments and low cooperation efficiency. This paper introduces a multi-agent cooperative task planning model with a novel half-cooperative DQN algorithm for dynamic target hunting in unknown environments. The model randomly assigns positions to agents and targets, defines the action space for agents, and designs reward functions for real-world situations. The half-cooperative DQN algorithm uses prioritized experience replay for efficient learning and employs a half-cooperative strategy to enhance cooperation among agents, thereby improving hunting efficiency. Experimental results show that the half-cooperative DQN algorithm outperforms other improved DQN algorithms in terms of success rate, average boundary violations, and average time steps, highlighting its advantages and potential in dynamic target hunting.

在救援、军事和执法等领域，有效的目标搜寻至关重要。目前的动态目标搜索算法在多目标场景下，由于环境未知和协作效率低，往往会出现失败的情况。提出了一种基于半合作DQN算法的多智能体协作任务规划模型，用于未知环境下的动态目标搜索。该模型为智能体和目标随机分配位置，定义智能体的动作空间，并设计现实情况下的奖励函数。半合作DQN算法采用优先经验重播进行高效学习，采用半合作策略增强agent间的合作，从而提高搜索效率。实验结果表明，半合作DQN算法在成功率、平均边界违反和平均时间步长等方面都优于其他改进的DQN算法，突出了其在动态目标搜索方面的优势和潜力。

引用次数: 0

SafeIL: Safety constrained imitation learning for autonomous systems 基于安全约束的自主系统模仿学习

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-02-03 DOI: 10.1016/j.robot.2026.105376

Gunmin Lee , Jaeseok Heo , Dohyeong Kim , Geunje Cheon , Jeongwoo Oh , Minyoung Hwang , Chanwoo Park , Kyungjae Lee , Songhwai Oh

Safety is a critical concern in controller design, yet developing a cost function that accurately reflects safety remains a significant challenge, akin to the complexities of designing a reward function. To address this, we introduce Safety Constrained Imitation Learning (SafeIL), an innovative safety-constrained imitation learning framework that simultaneously estimates reward and safety cost functions using two distinct sets of expert demonstrations: one aimed at maximizing rewards without considering safety, and the other focused on avoiding safety violations during execution. Through the use of dual independent discriminator networks, SafeIL effectively learns these functions, enabling the development of a controller that ensures safety while maintaining high performance. Our empirical evaluations across diverse simulation environments, including safety-gym, Metadrive, CARLA, F1tenth, and real-world platforms such as Jackal and RC car, demonstrate that SafeIL significantly outperforms existing methods like GAIL. Specifically, SafeIL achieves substantial reductions in constraint violations, including zero violations on the Jackal platform, and reduces violations by 79.6% compared to GAIL when utilizing safety-focused demonstrations, underscoring its potential to enhance safety in real-world robotic applications.

安全性是控制器设计中的一个关键问题，但开发一个准确反映安全性的成本函数仍然是一个重大挑战，类似于设计奖励函数的复杂性。为了解决这个问题，我们引入了安全约束模仿学习（SafeIL），这是一种创新的安全约束模仿学习框架，它使用两组不同的专家演示来同时估计奖励和安全成本函数：一组旨在在不考虑安全性的情况下最大化奖励，另一组侧重于在执行过程中避免违反安全。通过使用双独立鉴别器网络，SafeIL有效地学习了这些功能，使控制器的开发能够在保持高性能的同时确保安全。我们对各种模拟环境（包括safety-gym、Metadrive、CARLA、f110以及Jackal和RC car等现实世界平台）的经验评估表明，SafeIL显著优于GAIL等现有方法。具体来说，SafeIL实现了大幅减少约束违规，包括在Jackal平台上零违规，并且在使用以安全为重点的演示时，与GAIL相比，减少了79.6%的违规，强调了其在实际机器人应用中提高安全性的潜力。

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引用次数: 0

VLM-integrated 3D perception model for robust robotic grasping adapted to deformable sacks with arbitrary shapes 基于vlm集成的机器人抓取三维感知模型，适应任意形状的可变形麻袋

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.robot.2026.105372

Seongje Kim , Jonghun Yoon

This study presents a vision-language and 3D perception-integrated system for robust grasping of non-standard logistics parcels, including untrained boxes and deformable sacks. To address the limitations of conventional recognition models in handling unseen or irregularly shaped objects, we propose a zero-shot object recognition framework based on a Vision-Language Model (VLM), combined with confidence threshold optimization and Non-Maximum Suppression (NMS) to improve detection reliability. The system further incorporates 3D point cloud-based post-processing to extract precise grasp points adapted to both rigid and deformable object geometries. Proposed approach achieves a mean Average Precision (mAP) of 80.1 % for detecting untrained boxes and burlap sacks and demonstrates a grasp success rate of 99.0 % for boxes and 73.0 % for deformable sacks in real-world unloading scenarios using collaborative robots. Unlike prior methods that require extensive retraining for new object types, proposed system enables generalizable, real-time grasping without additional dataset preparation. The hybrid integration of rule-based and learning-based strategies in 3D space contributes to sTable and adapTable grasp point selection across variable object types. This work substantiates the feasibility of zero-shot recognition and grasping of non-standard logistics items, offering a practical solution for automated parcel unloading in dynamic industrial environments.

本研究提出了一种视觉语言和3D感知集成系统，用于抓取非标准物流包裹，包括未经训练的箱子和可变形的袋子。为了解决传统识别模型在处理不可见或不规则形状物体方面的局限性，我们提出了一种基于视觉语言模型（VLM）的零射击目标识别框架，结合置信度阈值优化和非最大抑制（NMS）来提高检测可靠性。该系统进一步结合了基于3D点云的后处理，以提取适合刚性和可变形物体几何形状的精确抓取点。该方法在检测未经训练的箱子和粗麻袋方面的平均精度（mAP）为80.1%，在使用协作机器人的实际卸载场景中，箱子和变形袋的抓取成功率分别为99.0%和73.0%。与之前需要对新对象类型进行大量再训练的方法不同，该系统无需额外的数据集准备即可实现可泛化的实时抓取。基于规则和基于学习的三维空间策略的混合集成有助于在不同对象类型中稳定和自适应地选择抓取点。本研究验证了非标物流物品零射击识别与抓取的可行性，为动态工业环境下的包裹自动卸载提供了一种实用的解决方案。

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引用次数: 0

Collision-free multi-UAV 3D path planning using dynamic spatial reservations of reducing air corridors 基于减少空中走廊的动态空间预留的无碰撞多无人机三维路径规划

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-20 DOI: 10.1016/j.robot.2026.105359

Andrey Ronzhin , Anton Saveliev , Dmitry Anikin , Alexandra Zaytseva , Ekaterina Cherskikh , Aleksandra Figurek

This paper presents a centralized coordination framework for multi-agent Unmanned Aerial Vehicle (UAV) systems, designed to ensure collision-free navigation in complex three-dimensional environments. The core contribution is a dynamic spatial reservation mechanism based on reducing air corridors. These defined, capsule-shaped volumes are allocated to each agent and contract along their longitudinal axis during traversal, thereby progressively releasing airspace for subsequent vehicles. To enhance safety during coordination failures or at predefined target points, agents are encapsulated within protective spherical zones. The coordination logic adheres to a first-come-first-served sequential planning paradigm, wherein agents employ an any-angle path planner. Generated paths are constrained to avoid both static obstacles and reserved corridors, while adhering to the agent's specific safe-radius constraints. In the event of a planning failure induced by spatial conflicts, agents enter a protective state and re-attempt path generation after a configurable delay. The collision detection subsystem implements a multi-layered approach for efficiency. Broad-phase filtering utilizes an octree for static obstacles, a hierarchical hash grid for dynamic corridors, and elevation bounds for terrain. The narrow phase employs analytical geometric verification, modeling static obstacles as convex polyhedra, dynamic corridors as capsules, and terrain as bilinear patches derived from elevation grids. This ensures precise, exact-distance computations for all collision checks. Experimental results validate the framework's efficacy in achieving collision-free coordination across a range of operational scenarios. Although computational load and agent waiting times exhibit a predictable increase with population density, the method provides formal guarantees of safety and mission completion through its rigorous validation and retry mechanisms. Consequently, the proposed approach is particularly suited for safety-critical applications where operational reliability is paramount.

针对多智能体无人机系统在复杂三维环境下的无碰撞导航问题，提出了一种集中协调框架。其核心贡献是基于减少空气走廊的动态空间保留机制。这些定义的胶囊状空间被分配给每个agent，并在穿越过程中沿着它们的纵轴收缩，从而逐步为后续车辆释放空间。为了在协调失败或在预定义的目标点时提高安全性，代理被封装在保护的球形区域内。协调逻辑遵循先到先服务的顺序规划范式，其中代理使用任意角度的路径规划器。生成的路径受到约束，以避免静态障碍物和保留的走廊，同时遵守代理的特定安全半径约束。在空间冲突导致规划失败的情况下，agent进入保护状态，并在可配置的延迟后重新尝试路径生成。为了提高效率，碰撞检测子系统采用了多层方法。宽相位滤波对静态障碍物使用八叉树，对动态走廊使用分层哈希网格，对地形使用高程边界。狭窄阶段采用解析几何验证，将静态障碍物建模为凸多面体，将动态走廊建模为胶囊，将地形建模为来自高程网格的双线性斑块。这确保了所有碰撞检查的精确、精确距离计算。实验结果验证了该框架在一系列操作场景中实现无碰撞协调的有效性。尽管计算负载和代理等待时间随着人口密度的增加而增加，但该方法通过其严格的验证和重试机制为安全性和任务完成提供了正式的保证。因此，所建议的方法特别适合于操作可靠性至关重要的安全关键应用程序。

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引用次数: 0

Offshore Subsea IMR Operations: Review of the Automation Potential 海上水下IMR作业：自动化潜力综述

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-05-01 Epub Date: 2026-01-27 DOI: 10.1016/j.robot.2026.105365

Jannic Schurmann Larsen, Simon Pedersen, Jesper Liniger, Fredrik Fogh Sørensen

The demand for green energy continues to rise, with predictions indicating a 62-fold growth in floating wind. This offshore expansion, especially from wind, requires a corresponding increase in inspection, maintenance, and repair (IMR) operations. For offshore turbines, the marine segment accounts for half of fixed turbine O&M costs. One avenue to reduce these costs is through unmanned underwater vehicles (UUV) and automation. UUVs are becoming more specialized and automated, but most tasks remain only partially automated. It is estimated that the global energy cost in 2050 can be reduced by 0.5–0.6% from increased unmanned underwater vehicle automation, with wind contributing 0.4–0.5%. This study assesses automation levels and task frequencies across application domains, defined as types of offshore infrastructure, to calculate an automation priority score (APS), a weighted product of automation level and task frequency, indicating where economic and development benefits are. The automation level refers to a UUVs ability to operate without human intervention while performing tasks, regardless of industry use. The current highest APS is found in pipelines and jacket structures, with the lowest in wind turbines and dams. A sensitivity analysis evaluates the effects of increasing automation levels and anticipated annual growth. The study concludes that the wind sector will represent the highest automation priority in the future, providing the greatest economic incentive, especially for mooring lines. Visual inspection will increase due to AI, crawler-based UUVs will dominate circular structures IMR tasks, and autonomous underwater vehicle (AUVs) with subsea stations will handle frequent or long-range tasks.

对绿色能源的需求持续增长，预测表明浮动风的需求将增长62倍。这种海上扩张，特别是风能的扩张，需要相应增加检查、维护和维修（IMR）操作。对于海上涡轮机，海上部分占固定涡轮机运营成本的一半。降低这些成本的一个途径是通过无人水下航行器（UUV）和自动化。uuv正变得越来越专业化和自动化，但大多数任务仍然只是部分自动化。据估计，到2050年，无人潜航器自动化程度的提高可使全球能源成本降低0.5-0.6%，其中风能贡献0.4-0.5%。本研究评估了跨应用领域（定义为离岸基础设施类型）的自动化水平和任务频率，以计算自动化优先级评分（APS），这是自动化水平和任务频率的加权乘积，表明经济和发展效益在哪里。自动化级别指的是uuv在执行任务时无需人工干预的能力，无论行业用途如何。目前，管道和导管结构的APS最高，风力涡轮机和水坝的APS最低。敏感性分析评估了自动化水平提高和预期年增长的影响。该研究的结论是，风能行业将代表未来自动化的最高优先级，提供最大的经济激励，特别是对系泊线。由于人工智能，视觉检查将会增加，基于履带式的uuv将主导圆形结构的IMR任务，而带有海底站的自主水下航行器（auv）将处理频繁或远程任务。

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引用次数: 0

Terrain-based place recognition for LiDAR SLAM of quadruped robots with limited field-of-view measurements 有限视场测量条件下四足机器人激光雷达SLAM的地形位置识别

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.robot.2025.105315

Roun Lee , Seonghun Hong , Sukmin Yoon

Over the past few decades, light detection and ranging (LiDAR) sensors have been extensively employed for pose estimation in simultaneous localization and mapping (SLAM). In more recent years, the use of solid-state LiDAR sensors with no rotating mechanisms and a limited field-of-view for SLAM has attracted research attention because of their cost effectiveness and durability. However, it is highly challenging to successfully perform place recognition, which is one of the most important components of SLAM, via limited field-of-view measurements. Failure in place recognition can severely degrade the resulting estimation performance of SLAM algorithms. Considering a terrestrial SLAM framework for quadruped robots with limited field-of-view LiDAR sensors, this study proposes a terrain-based place recognition algorithm that reconstructs and compares detected feature terrains, using a set of foot contact information for quadruped robots. The validity and practical feasibility of the proposed approach are demonstrated through experimental results using a quadruped robot system with a limited field-of-view LiDAR sensor.

在过去的几十年里，光探测和测距（LiDAR）传感器被广泛应用于同步定位和测绘（SLAM）中的姿态估计。近年来，使用无旋转机构和有限视场的固态激光雷达传感器进行SLAM由于其成本效益和耐用性而引起了研究的关注。然而，通过有限的视场测量，成功地进行位置识别是SLAM最重要的组成部分之一，这是非常具有挑战性的。位置识别失败会严重降低SLAM算法的估计性能。考虑到具有有限视场LiDAR传感器的四足机器人的地面SLAM框架，本研究提出了一种基于地形的位置识别算法，该算法使用一组四足机器人的足部接触信息来重建和比较检测到的特征地形。实验结果表明，采用有限视场激光雷达传感器的四足机器人系统验证了该方法的有效性和实际可行性。

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引用次数: 0

Exploiting Euclidean distance field properties for fast and safe 3D planning with a modified Lazy Theta* 利用欧几里得距离场属性，使用改进的Lazy Theta*进行快速安全的3D规划

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems

Pub Date : 2026-04-01 Epub Date: 2025-12-29 DOI: 10.1016/j.robot.2025.105317

Jose A. Cobano, L. Merino, F. Caballero

This paper presents the FS-Planner, a fast graph-search planner based on a modified Lazy Theta* algorithm that exploits the analytical properties of Euclidean Distance Fields (EDFs). We introduce a new cost function that integrates an EDF-based term proven to satisfy the triangle inequality, enabling efficient parent selection and reducing computation time while generating safe paths with smaller heading variations. We also derive an analytic approximation of the EDF integral along a segment and analyse the influence of the line-of-sight limit on the approximation error, motivating the use of a bounded visibility range. Furthermore, we propose a gradient-based neighbour-selection mechanism that decreases the number of explored nodes and improves computational performance without degrading safety or path quality. The FS-Planner produces safe paths with small heading changes without requiring the use of post-processing methods. Extensive experiments and comparisons in challenging 3D indoor simulation environments, complemented by tests in real-world outdoor environments, are used to evaluate and validate the FS-Planner. The results show consistent improvements in computation time, exploration efficiency, safety, and smoothness in a geometric sense compared with baseline heuristic planners, while maintaining sub-optimality within acceptable bounds. Finally, the proposed EDF-based cost formulation is orthogonal to the underlying search method and can be incorporated into other planning paradigms.

本文提出了一种基于改进的Lazy Theta*算法的快速图搜索规划器FS-Planner，该算法利用了欧几里得距离场（edf）的解析特性。我们引入了一个新的成本函数，该函数集成了一个基于edf的项，该项已被证明满足三角形不等式，能够有效地选择父路径，减少计算时间，同时生成具有较小航向变化的安全路径。我们还推导了EDF积分沿一段的解析近似，并分析了视距限制对近似误差的影响，从而激发了有界可见范围的使用。此外，我们提出了一种基于梯度的邻居选择机制，该机制在不降低安全性或路径质量的情况下减少了探索节点的数量并提高了计算性能。FS-Planner在不需要使用后处理方法的情况下，产生具有小标题变化的安全路径。在具有挑战性的3D室内模拟环境中进行了大量的实验和比较，并在真实的室外环境中进行了测试，以评估和验证FS-Planner。结果表明，与基线启发式规划器相比，在计算时间、勘探效率、安全性和几何意义上的平滑性方面有了一致的改进，同时在可接受的范围内保持了次优性。最后，提出的基于edf的成本公式与基础搜索方法正交，可以纳入其他规划范例。

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引用次数: 0