Autonomous Robots最新文献

Optimal policies for autonomous navigation in strong currents using fast marching trees 利用快速行进树在强水流中实现自主导航的最佳策略

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-22 DOI: 10.1007/s10514-024-10179-z

Bernardo Martinez Rocamora Jr., Guilherme A. S. Pereira

引用次数: 0

Correction: Planning under uncertainty for safe robot exploration using gaussian process prediction 更正：利用高斯过程预测在不确定条件下为机器人安全探索制定计划

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-17 DOI: 10.1007/s10514-024-10181-5

Alex Stephens, Matthew Budd, Michal Staniaszek, Benoit Casseau, Paul Duckworth, Maurice Fallon, Nick Hawes, Bruno Lacerda

引用次数: 0

A concurrent learning approach to monocular vision range regulation of leader/follower systems 领导者/追随者系统单目视觉范围调节的并发学习方法

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-17 DOI: 10.1007/s10514-024-10178-0

Luisa Fairfax, Patricio Vela

{"title":"A concurrent learning approach to monocular vision range regulation of leader/follower systems","authors":"Luisa Fairfax, Patricio Vela","doi":"10.1007/s10514-024-10178-0","DOIUrl":"10.1007/s10514-024-10178-0","url":null,"abstract":"<div><p>This paper explores range and bearing angle regulation of a leader–follower using monocular vision. The main challenge is that monocular vision does not directly provide a range measurement. The contribution is a novel concurrent learning (CL) approach, called CL Subtended Angle and Bearing Estimator for Relative pose (CL-SABER), which achieves range regulation without communication, persistency of excitation or known geometry and is demonstrated on a physical, robot platform. A history stack estimates target size which augments the Kalman filter (KF) with a range pseudomeasurement. The target is followed <i>to scale without drift, persistency of excitation requirements, prior knowledge, or additional measurements</i>. <i>Finite</i> excitation is required to achieve parameter convergence and perform steady-state regulation using CL-SABER. Evaluation using simulation and mobile robot experiments in special Euclidean planar space (<i>SE</i>(2)) show that the new method provides stable and consistent range regulation, as demonstrated by the inter-rater reliability, including in noisy and high leader acceleration environments.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10178-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous planning for inertial-aided systems 惯性辅助系统的连续规划

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-12 DOI: 10.1007/s10514-024-10180-6

Mitchell Usayiwevu, Fouad Sukkar, Chanyeol Yoo, Robert Fitch, Teresa Vidal-Calleja

{"title":"Continuous planning for inertial-aided systems","authors":"Mitchell Usayiwevu, Fouad Sukkar, Chanyeol Yoo, Robert Fitch, Teresa Vidal-Calleja","doi":"10.1007/s10514-024-10180-6","DOIUrl":"10.1007/s10514-024-10180-6","url":null,"abstract":"<div><p>Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories that aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the <span>(hbox {RRT}^*)</span> planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real-world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10180-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic event-triggered integrated task and motion planning for process-aware source seeking 面向过程感知寻源的动态事件触发式综合任务和运动规划

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-12 DOI: 10.1007/s10514-024-10177-1

Yingke Li, Mengxue Hou, Enlu Zhou, Fumin Zhang

{"title":"Dynamic event-triggered integrated task and motion planning for process-aware source seeking","authors":"Yingke Li, Mengxue Hou, Enlu Zhou, Fumin Zhang","doi":"10.1007/s10514-024-10177-1","DOIUrl":"10.1007/s10514-024-10177-1","url":null,"abstract":"<div><p>The process-aware source seeking (PASS) problem in flow fields aims to find an informative trajectory to reach an unknown source location while taking the energy consumption in the flow fields into consideration. Taking advantage of the dynamic flow field partition technique, this paper formulates this problem as a task and motion planning (TAMP) problem and proposes a bi-level hierarchical planning framework to decouple the planning of inter-region transition and inner-region trajectory by introducing inter-region junctions. An integrated strategy is developed to enable efficient upper-level planning by investigating the optimal solution of the lower-level planner. In order to leverage the information acquisition and computational burden, a dynamic event-triggered mechanism is introduced to enable asynchronized estimation, region partitioning and re-plans. The proposed algorithm provides guaranteed convergence of the trajectory, and achieves automatic trade-offs of both exploration-exploitation and accuracy-efficiency. Simulations in a highly complicated and realistic ocean surface flow field validate the merits of the proposed algorithm, which demonstrates a significant reduction in computational burden without compromising planning optimality.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10177-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach 基于光流的微型飞行器控制：一种高效的数据驱动增量非线性动态反演方法

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-06 DOI: 10.1007/s10514-024-10174-4

Hann Woei Ho, Ye Zhou, Yiting Feng, Guido C. H. E. de Croon

{"title":"Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach","authors":"Hann Woei Ho, Ye Zhou, Yiting Feng, Guido C. H. E. de Croon","doi":"10.1007/s10514-024-10174-4","DOIUrl":"10.1007/s10514-024-10174-4","url":null,"abstract":"<div><p>This paper proposes an innovative approach for optical flow-based control of micro air vehicles (MAVs), addressing challenges inherent in the nonlinearity of optical flow observables. The proposed incremental nonlinear dynamic inversion (INDI) control scheme employs an efficient data-driven approach to directly estimate the inverse of the time-varying INDI control effectiveness in real-time. This method eliminates the constant effectiveness assumption typically made by traditional INDI methods and reduces the computational burden associated with inverting this variable at each time step. It effectively handles rapidly changing system dynamics, often encountered in optical flow-based control, particularly height-dependent control variables. Stability analysis of the proposed control scheme is conducted, and its robustness and efficiency are demonstrated through both numerical simulations and real-world flight tests. These tests include multiple landings of an MAV on a static, flat surface with several different tracking setpoints, as well as hovering and landings on moving and undulating surfaces. Despite the challenges posed by noisy optical flow estimates and lateral or vertical movements of the landing surfaces, the MAV successfully tracks or lands on the surface with an exponential decay of both height and vertical velocity almost simultaneously, aligning with the desired performance.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142410300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual asymmetric limit surfaces and their applications to planar manipulation 双非对称极限表面及其在平面操纵中的应用

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-10-01 DOI: 10.1007/s10514-024-10173-5

Xili Yi, An Dang, Nima Fazeli

引用次数: 0

Bridging the reality gap in drone swarm development through mixed reality 通过混合现实技术弥合无人机群开发中的现实差距

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-09-30 DOI: 10.1007/s10514-024-10169-1

Micha Sende, Christian Raffelsberger, Christian Bettstetter

引用次数: 0

TIP: A trust inference and propagation model in multi-human multi-robot teams TIP：多人多机器人团队中的信任推断和传播模型

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-09-30 DOI: 10.1007/s10514-024-10175-3

Yaohui Guo, X. Jessie Yang, Cong Shi

{"title":"TIP: A trust inference and propagation model in multi-human multi-robot teams","authors":"Yaohui Guo, X. Jessie Yang, Cong Shi","doi":"10.1007/s10514-024-10175-3","DOIUrl":"10.1007/s10514-024-10175-3","url":null,"abstract":"<div><p>Trust is a crucial factor for effective human–robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (<span>(N=30)</span>). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10175-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Planning under uncertainty for safe robot exploration using Gaussian process prediction 利用高斯过程预测进行不确定性下的规划，实现机器人安全探索

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-08-28 DOI: 10.1007/s10514-024-10172-6

Alex Stephens, Matthew Budd, Michal Staniaszek, Benoit Casseau, Paul Duckworth, Maurice Fallon, Nick Hawes, Bruno Lacerda

{"title":"Planning under uncertainty for safe robot exploration using Gaussian process prediction","authors":"Alex Stephens, Matthew Budd, Michal Staniaszek, Benoit Casseau, Paul Duckworth, Maurice Fallon, Nick Hawes, Bruno Lacerda","doi":"10.1007/s10514-024-10172-6","DOIUrl":"10.1007/s10514-024-10172-6","url":null,"abstract":"<div><p>The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10172-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0