Article
Robotics
Nikita Rudin, Hendrik Kolvenbach, Vassilios Tsounis, Marco Hutter
Summary: This article demonstrates the application of learned policies to solve legged locomotion control tasks, using deep reinforcement learning to train a neural network to control a jumping quadruped robot in both simulated and real-world low-gravity environments. The study shows successful generalization and deployment of trained policies for repetitive controlled jumping and landing with natural agility.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Bowen Yang, Qingwen Zhang, Ruoyu Geng, Lujia Wang, Ming Liu
Summary: Having good knowledge of terrain information is essential for improving the performance of legged robots in locomotion and navigation on complex terrains. We present a novel framework that generates dense robot-centric elevation maps online from sparse LiDAR observations, and provides uncertainty estimations. Our approach ensures high robustness and computational efficiency by using a novel pre-processing and point features representation approach. The generative Bayesian model recovers detailed terrain structures and provides pixel-wise reconstruction uncertainty, benefiting the downstream tasks of legged robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Malte Schilling, Jan Paskarbeit, Helge Ritter, Axel Schneider, Holk Cruse
Summary: Animal locomotion provides a model for adaptive behavior, with insects showing that flexibility results from a modular architecture and neural network. A control system implemented on a hexapod robot handles various walking patterns and proposes a cognitive expansion to deal with novel situations. Internal simulation-based planning is used when the model-free controller fails, demonstrating feasibility in walking over uncertain terrain in three scenarios.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Kyeong-Won Park, Jungsu Choi, Kyoungchul Kong
Summary: This article presents an adaptive gait pattern adjustment method for powered exoskeletons worn by individuals with complete paraplegia. The method adjusts joint-reference trajectories based on the timing of ground contact, aiming to achieve a natural gait motion. Experimental results demonstrate the excellent performance of the proposed method in various aspects.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Robotics
Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, Jemin Hwangbo
Summary: This research proposes a locomotion training framework where control policy and state estimator are trained concurrently. The framework utilizes a fast simulation environment to train the networks, which are then transferred to a real robot. The trained policy and state estimator can traverse diverse terrains and achieve high speeds under different conditions.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Philipp Schillinger, Sergio Garcia, Alexandros Makris, Konstantinos Roditakis, Michalis Logothetis, Konstantinos Alevizos, Wei Ren, Pouria Tajvar, Patrizio Pelliccione, Antonis Argyros, Kostas J. Kyriakopoulos, Dimos Dimarogonas
Summary: Efficiently coordinating different types of robots is crucial for commercial and industrial automation tasks. The distributed framework presented in this paper enables a team of heterogeneous robots to dynamically generate actions from a common, user-defined goal specification. This framework integrates various robotic capabilities and is suitable for a wide range of real-world tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2021)
Article
Robotics
Gabriel Aguirre-Ollinger, Haoyong Yu
Summary: A novel force control method for a SEA-driven lower-limb assistive exoskeleton is presented in this study, featuring alternating between different stiffness levels to control force, which can help correct the asymmetric gait typical of stroke survivors.
IEEE TRANSACTIONS ON ROBOTICS
(2021)
Article
Chemistry, Analytical
Timothy Sands
Summary: This study compares methods for improving the performance of existing surgical robotic systems, including prefiltering, sensor fusion, and fictional forces for displacement measurement. The best performing method, called prefiltered open-loop optimal + transport decoupling, achieves 1-3% attitude tracking performance of the robotic instrument with a two percent reduced computational burden and without increased costs.
Article
Automation & Control Systems
Bingchen Jin, Shusheng Ye, Juntong Su, Jianwen Luo
Summary: This article introduces an online identification method and adaptive control algorithm for quadruped robot locomotion to address model uncertainties and severe disturbances caused by heavy payload. Experimental results verify the feasibility of this method in estimating the disturbances induced by different payload weights.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Mechanical
Antonio Cardenas, Osmar Quiroz, Ricardo Hernandez, Hugo Medellin-Castillo, Alejandro Gonzalez, Mauro Maya, Davide Piovesan
Summary: This study introduces the design, development, and control of a new autonomous mobile manipulator for unstructured terrain, featuring an innovative suspension system and vision-based navigation. The mobile robot, fabricated using additive manufacturing techniques, demonstrates excellent traversal capability and controllability in rough terrains.
JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME
(2021)
Article
Computer Science, Artificial Intelligence
Changxin Huang, Guangrun Wang, Zhibo Zhou, Ronghui Zhang, Liang Lin
Summary: Controlling a non-statically bipedal robot is challenging, but this study proposes a novel reward-adaptive reinforcement learning method for biped locomotion. By using a dynamic mechanism, the control policy can be simultaneously optimized by multiple criteria. The proposed method utilizes a multi-head critic to learn a separate value function for each reward component, resulting in hybrid policy gradients. Experimental results demonstrate the effectiveness and generalization of this approach.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Robotics
Chen Yu, Andre Rosendo
Summary: This paper proposes a multi-modal locomotion framework that combines hand-crafted transition motion and a learning-based bipedal controller to enable quadruped robots to walk on two legs. Experimental results show that the framework performs well in both simulation and real-world settings.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Avadesh Meduri, Paarth Shah, Julian Viereck, Majid Khadiv, Ioannis Havoutis, Ludovic Righetti
Summary: This work proposes a nonlinear model predictive control (MPC) framework, BiConMP, for online generation of whole-body trajectories for legged robots. The framework efficiently exploits the structure of robot dynamics to generate various cyclic gaits on a real quadruped robot and evaluates its performance on different terrains, countering unforeseen pushes, and transitioning online between different gaits. The ability of BiConMP to generate nontrivial acyclic whole-body dynamic motions on the robot is also presented. The same approach is applied to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. An extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Computer Science, Artificial Intelligence
Hiroyasu Nakano, Ryo Ariizumi, Toru Asai, Shun-Ichi Azuma
Summary: In this article, a novel reinforcement learning algorithm called PI2-CMA is proposed to optimize the continuous behavior of robots. The algorithm solves the problem of tuning the temperature parameter in the existing method through automatic adjustment. The effectiveness of the proposed method is confirmed through numerical tests.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Robotics
Zhongyu Li, Jun Zeng, Shuxiao Chen, Koushil Sreenath
Summary: This paper presents an end-to-end autonomous navigation framework for bipedal robots to safely explore height-constrained environments. It leverages three layers of planners and a variable walking height controller to optimize trajectory plans and maintain stable periodic walking gaits. Experimental results with a bipedal robot Cassie demonstrate the reliability of the framework in avoiding obstacles and reaching the goal location in various cluttered environments.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2023)
Article
Automation & Control Systems
Runwei Guan, Shanliang Yao, Lulu Liu, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee, Yutao Yue
Summary: With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant. Traditional RGB cameras cannot work effectively in adverse weather and at night, which has led to the emergence of 4D millimeter-wave radar as a new perception sensor. However, the radar suffers from water-surface clutter and irregular shape of point cloud. To address these issues, this paper proposes a high-performance panoptic perception model called Mask-VRDet, which fuses features of vision and radar using graph neural network.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Adrien Le Reun, Kevin Subrin, Anthony Dubois, Sebastien Garnier
Summary: This study aims to evaluate the quality and health of aerospace parts using a high-dimensional robotic cell. By utilizing X-ray Computed Tomography devices, the interior of the parts can be reconstructed and anomalies can be detected. A methodology is proposed to assess both the raw process capability and the improved process capability, with three strategies developed to improve the robot behavior model and calibration.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Weiming Ba, Jung-Che Chang, Jing Liu, Xi Wang, Xin Dong, Dragos Axinte
Summary: This paper proposes a hybrid scheme for kinematic control of continuum robots, which avoids errors through tension supervision and accurate piecewise linear approximation. The effectiveness of the controller is verified on different continuum robotic systems.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, Antonio Paolillo
Summary: In this study, a learning-based approach is proposed to predict the probability of human users interacting with a robot before the interaction begins. By considering the pose and motion of the user, the approach labels the robot's encounters with humans in a self-supervised manner. The method is validated and deployed in various scenarios, achieving high accuracy in predicting user intentions to interact with the robot.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Tiago Cortinhal, Eren Erdal Aksoy
Summary: This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation between LiDAR and camera sensors. The model is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments, and it has practical applications in fields like autonomous vehicles.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis
Summary: This paper addresses the issue of informative path planning for a UAV used in precision agriculture. By using a non-uniform scanning approach, the time spent in areas with minimal value is reduced, while maintaining high precision in information-dense regions. A novel active sensing and deep learning-based coverage path planning approach is proposed, which adjusts the UAV's speed based on the quantity and confidence level of identified plant classes.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shota Kokubu, Pablo E. Tortos Vinocour, Wenwei Yu
Summary: In this study, a new modular soft actuator was proposed to improve the support performance of soft rehabilitation gloves (SRGs). Objective evaluations and clinical tests were conducted to demonstrate the effectiveness and functionality of the proposed actuator and SRG.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Jinliang Zhu, Yuanxi Sun, Jie Xiong, Yiyang Liu, Jia Zheng, Long Bai
Summary: This paper proposes an active prosthetic knee joint with a variable stiffness parallel elastic actuation mechanism. Numerical verifications and practical experiments demonstrate that the mechanism can reduce torque and power, thus reducing energy consumption and improving the endurance of the prosthetic knee joint.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Yong You, Jingtao Wu, Yunlong Meng, Dongye Sun, Datong Qin
Summary: A new power-cycling variable transmission (PCVT) is proposed and applied to construction vehicles to improve transmission efficiency. A shift correction strategy is developed based on identifying the changes in construction vehicles' mass and gradient. Simulation results show that the proposed method can correct shift points, improve operation efficiency, and ensure a safer operation process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shaorui Liu, Wei Tian, Jianxin Shen, Bo Li, Pengcheng Li
Summary: This paper proposes a two-objective optimization technique for multi-robot systems, addressing the issue of balancing productivity and machining performance in high-quality machining tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pengchao Ding, Faben Zhu, Hongbiao Zhu, Gongcheng Wang, Hua Bai, Han Wang, Dongmei Wu, Zhijiang Du, Weidong Wang
Summary: We propose an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme includes stair localization, structural parameter estimation, and optimization of the approaching process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pedro Azevedo, Vitor Santos
Summary: Accurate detection and tracking of vulnerable road users and traffic objects are vital tasks for autonomous driving and driving assistance systems. This paper proposes a solution for object detection and tracking in an autonomous driving scenario, comparing different object detectors and exploring the deployment on edge devices. The effectiveness of DeepStream technology and different object trackers is assessed using the KITTI tracking dataset.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Benjamin Beiter, Divya Srinivasan, Alexander Leonessa
Summary: Powered exoskeletons can significantly reduce physical workload and have great potential impact on future labor practices. To truly assist users in achieving task goals, a shared autonomy control framework is proposed to separate the control objectives of the human and exoskeleton. Positive Power control is introduced for the human-based controller, while 'acceptance' is used as a measure of matching the exoskeleton's control objective to the human's. Both control objectives are implemented in an optimization-based Whole-Body-Control structure. The results verify the effectiveness of the control framework and its potential for improving cooperative control for powered exoskeletons.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)