Article
Automation & Control Systems
Ran Liu, Yongping He, Chau Yuen, Billy Pik Lik Lau, Rashid Ali, Wenpeng Fu, Zhiqiang Cao
Summary: This article discusses the challenges and solution for environment mapping in unknown and featureless environments using low-cost 2D LiDARs. The proposed approach combines ultrawideband (UWB) with 2D LiDARs to improve mapping quality of mobile robots by optimizing trajectory and incorporating LiDAR-based loop closures. The results show a significant reduction in mapping error compared to the conventional GMapping algorithm with short-range LiDAR.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Aerospace
Kyle R. Williams, Rachel Schlossman, Daniel Whitten, Joe Ingram, Srideep Musuvathy, James Pagan, Kyle A. Williams, Sam Green, Anirudh Patel, Anirban Mazumdar, Julie Parish
Summary: This article introduces a trajectory planning technique based on parameterized high-level actions. The use of high-level actions improves the performance of reinforcement learning (RL) guidance policies by reducing training steps and increasing path performance. The method is demonstrated on a space-shuttle guidance example, showing improved performance compared to a baseline RL implementation. The article also develops a loss function term for deep RL that enhances policy return.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Computer Science, Software Engineering
Amin Babadi, Michiel van de Panne, C. Karen Liu, Perttu Hamalainen
Summary: This article proposes a novel method for exploring the dynamics of physically based animated characters and learning a task-agnostic action space to facilitate movement optimization. The method parameterizes actions as target states and learns a low-level control policy that drives the agent's state towards the targets. The approach improves trajectory and high-level policy optimization efficiency across multiple tasks and algorithms and provides visualizations demonstrating the benefits of using target states as actions.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Information Systems
Xiao Hu, Heng Wu, Qianlai Sun, Jun Liu
Summary: This paper proposes a time-optimal trajectory planning algorithm based on improved simplified particle swarm optimization (ISPSO) to solve the robot trajectory planning problem with the optimization goal of short running time. The robot's trajectory is constructed by 3-5-3 polynomial interpolation in the joint space of the robot. The objective function is constructed by the sum of the time intervals between each node while satisfying the velocity constraint. ISPSO is used to optimize the objective function by improving the inertia weight updating method and introducing a golden sine segmentation algorithm as an optimization operator. Compared with other particle swarm optimization algorithms, ISPSO demonstrates higher search velocity and accuracy. The effectiveness of the proposed algorithm is demonstrated through simulations using the PUMA 560 industrial robot, showing a 19% reduction in time compared to the simplified particle swarm algorithm. The simulation results prove that ISPSO achieves time optimization under the condition of velocity constraint, indicating its superiority in trajectory planning.
Article
Automation & Control Systems
Ying Liu, Junyi Tao, Bin He, Yu Zhang, Weichen Dai
Summary: This article proposes an efficient localization-oriented 3-D lidar map compression algorithm. The algorithm utilizes a multipose lidar sampling model based on feasible regions to include observation data on multiple trajectories in the compressed map. It also introduces a localization error sensitivity analysis to score the map points and calculates their localization contribution. The algorithm achieves high localization accuracy even when the compression ratio drops to 0.1%.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Robotics
Brendon Forsgren, Kevin Brink, Prashant Ganesh, Timothy W. W. McLain
Summary: This paper investigates the problems of cycle basis construction and sparsity maximization in relative pose graph optimization, and validates an algorithm's performance compared to the minimum cycle basis. Furthermore, a new methodology is introduced to enable the use of lower-degree-of-freedom measurements in the relative pose graph optimization problem. Extensive validation of the algorithms is conducted on standard benchmarks, simulated datasets, and custom hardware.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Zhengcai Cao, Dong Zhang, MengChu Zhou
Summary: This study investigates direction control and path following of a 3-D snake-like robot, proposing a new direction control method and an adaptive path-following algorithm that outperform classical methods. Simulation and experimental results demonstrate the superior performance of the proposed 3-D model and control methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Rana Azzam, Felix H. Kong, Tarek Taha, Yahya Zweiri
Summary: The paper proposes a graph neural network based on PoseConv for pose-graph classification, achieving 92-98% accuracy in testing and significantly faster processing speeds compared to other methods. The model is able to generalize to previously unseen variants of pose-graphs.
Article
Automation & Control Systems
Daniel Gleeson, Stefan Jakobsson, Raad Salman, Fredrik Ekstedt, Niklas Sandgren, Fredrik Edelvik, Johan S. Carlson, Bengt Lennartson
Summary: Spray painting plays an important role in the manufacturing industry, especially in the automotive sector. This article presents a novel method for optimizing the spray paint process by smoothing out the trajectory and minimizing paint thickness deviations from the target. The algorithm has been proven effective in both simple test cases and complex industrial scenarios.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Zikang Yuan, Ken Cheng, Jinhui Tang, Xin Yang
Summary: RGB-D DSO is an RGB-D direct sparse odometry method that addresses the performance degradation issue of existing RGB-D VO systems when large occlusions and a large portion of invalid depth values are present by utilizing sliding-window optimization, occlusion removal, and depth refinement modules.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Chemistry, Analytical
Martina Benko Loknar, Gregor Klancar, Saso Blazic
Summary: This paper addresses the problem of minimum-time smooth trajectory planning for wheeled mobile robots. A novel solution for constructing a 5th order Bezier curve is proposed, which allows for simple and intuitive parameterization. The trajectory optimization takes into account environmental constraints and constraints on velocity, acceleration, and jerk. The effectiveness of the proposed algorithm is demonstrated through simulations in a racetrack and warehouse environment, showing its applicability in constrained environments and real-world driving scenarios.
Article
Automation & Control Systems
Rishi K. Malhan, Shantanu Thakar, Ariyan M. Kabir, Pradeep Rajendran, Prahar M. Bhatt, Satyandra K. Gupta
Summary: This research presents an iterative graph construction method to find trajectories for semi-constrained Cartesian paths that use multiple tool center points (TCPs). The method finds near-optimal solutions with significantly fewer nodes and edges in the graph. The performance results of the algorithm on complex industrial test cases are provided.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Fangbin Wang, Yefei Gao, Zhong Chen, Xue Gong, Darong Zhu, Wanlin Cong
Summary: This study aims to improve the safety and efficiency of inspection robots for solar power plants. Through the improvement of the RRT* algorithm, a method based on adaptive target bias and heuristic circular sampling is proposed. The experimental results show that the improved algorithm significantly reduces search time, iterations, and path cost, providing a theoretical basis for enhancing the operational efficiency of inspection robots for solar power plants.
Article
Robotics
Jesus Tordesillas, Jonathan P. How
Summary: MADER is a 3-D decentralized and asynchronous trajectory planner for UAVs that generates collision-free trajectories in complex environments. It uses the MINVO basis to obtain smaller outer polyhedral representations, and guarantees safety with respect to other agents through a collision check-recheck scheme.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Computer Science, Information Systems
Maximilian Kraemer, Torsten Bertram
Summary: Trajectory optimization is a promising method for planning robotic manipulator trajectories, especially in dynamic environments with collaborative robots. This paper introduces two approaches to improve the quality of trajectory optimization: Extended Initialization reduces the risk of local minima, while globally guiding local solutions mitigates critical cases of standstills.
Article
Computer Science, Artificial Intelligence
Kun Cao, Lihua Xie
Summary: This paper proposes a game-theoretic inverse reinforcement learning framework that aims to learn the parameters of multistage games from demonstrated trajectories. The framework differentiates the Pontryagin's maximum principle equations of open-loop Nash equilibrium to solve the problem and can be solved through explicit recursions. Simulation examples demonstrate the effectiveness of the proposed algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Hanfeng Li, Lihua Xie, Xianfu Zhang, Weihao Pan
Summary: This article focuses on the distributed consensus control problem for nonlinear multi-agent systems subject to sensor uncertainty. A new time-varying gain approach is proposed to achieve leader-follower consensus of nonlinear multi-agent systems and handle the unknown growth rate and uncertain sensor sensitivity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Jianfei Yang, Jiangang Yang, Shizheng Wang, Shuxin Cao, Han Zou, Lihua Xie
Summary: This article proposes a new method, Cluster-level Discrepancy Minimization (CDM), for addressing domain adaptation problems with label shift and covariate shift. By learning tight and discriminative clusters, CDM can minimize discrepancies at both feature and distribution levels, thus alleviating the negative effect of label shift on domain transfer. Extensive experiments demonstrate significant improvements of CDM on imbalanced datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Jitao Li, Zhenhua Wang, Yi Shen, Lihua Xie
Summary: This article studies the security synthesis of cyber-physical systems subject to stealthy attacks using zonotopic set theory. The set is used to measure the impact of potential stealthy attacks on the systems. Control performance and security level are described using L-infinity performance index and the radius of the attack-induced state set. Sufficient design conditions are provided to optimize security while ensuring a specified level of control performance. Simulation examples are conducted to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lang Deng, Jianfei Yang, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie
Summary: In this article, a novel multimodal gait recognition method called GaitFi is proposed, which combines WiFi signals and videos for human identification. The GaitFi outperforms state-of-the-art gait recognition methods and achieves excellent results in real-world experiments.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl Henrik Johansson
Summary: This article discusses the problem of distributed online convex optimization with time-varying constraints over a network of agents. Two distributed online algorithms with full-information and bandit feedback are proposed. Network-wide loss and network cumulative constraint violation are used as measures, and theoretical analyses show the effectiveness of the proposed algorithms. Numerical simulations are provided to illustrate the results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Ci Chen, Lihua Xie, Yi Jiang, Kan Xie, Shengli Xie
Summary: In this article, the optimal output tracking problem for linear discrete-time systems with unknown dynamics is investigated using reinforcement learning (RL) and robust output regulation theory. Different from most existing works, which depend on the state of the system, this problem only utilizes the outputs of the reference system and the controlled system. The proposed off-policy RL algorithm allows for solving the output tracking problem using only measured output data and the reference output, without requiring complete and accurate system dynamics knowledge.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Robotics
Thien-Minh Nguyen, Daniel Duberg, Patric Jensfelt, Shenghai Yuan, Lihua Xie
Summary: This paper proposes an octree-based global map of multi-scale surfels that can be updated incrementally. It also introduces a point-to-surfel association scheme and continuous-time optimization to achieve Lidar-Inertial continuous-time odometry and mapping. Experimental results demonstrate the advantages of this system compared to other state-of-the-art methods.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Maopeng Ran, Shuai Feng, Juncheng Li, Lihua Xie
Summary: This article addresses the problem of quantized consensus for uncertain nonlinear multiagent systems under data-rate constraints and denial-of-service (DoS) attacks. A novel dynamic quantization method with zooming-in and holding capabilities is proposed to mitigate the effects of DoS attacks. The developed control protocol is shown to handle any DoS attacks inducing bounded consecutive packet losses with only three-level quantization, as demonstrated through experiments.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Yongyang Xiong, Ligang Wu, Keyou You, Lihua Xie
Summary: This article proposes a novel quantized distributed gradient tracking algorithm (Q-DGT) to address the bottleneck of communication efficiency in distributed networks. The algorithm achieves linear convergence and the numerical results confirm its efficiency.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Robotics
Thien Hoang Nguyen, Lihua Xie
Summary: In this article, the problem of estimating the four-degree-of-freedom robot-to-robot relative frame transformation using onboard odometry and interrobot distance measurements is studied. The theoretical analysis including the derivation and interpretation of the Cramer-Rao lower bound, the Fisher information matrix, and its determinant is presented. Optimization-based solutions, including a quadratically constrained quadratic programming formulation and its semidefinite programming relaxation, are proposed. Based on the theoretical results, singular configurations can be detected and the uncertainty of each parameter can be measured. Extensive simulations and real-life experiments show that the proposed methods outperform state-of-the-art approaches, especially in geometrically poor or large measurement noise conditions. The QCQP method provides superior results at the expense of computational time, while the SDP method is faster and sufficiently accurate in most cases.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Robotics
Muqing Cao, Kun Cao, Shenghai Yuan, Thien-Minh Nguyen, Lihua Xie
Summary: In this paper, a complete approach is proposed to address the challenging problem of planning multiple tethered robots to reach their individual targets without entanglements. A multi-robot tether-aware representation of homotopy is introduced to evaluate the feasibility and safety of potential paths, and a decentralized and online planning framework is applied to generate entanglement-free, collision-free and dynamically feasible trajectories. Simulations and flight experiments validate the effectiveness and practicality of the presented approach.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Remote Sensing
Cheng Cheng, Xiuxian Li, Lihua Xie, Li Li
Summary: This study proposes a navigation and landing scheme for UAVs to autonomously land on moving UGVs in GPS-denied environments using vision, UWB, and system information. The position estimation of the UAV relative to the target is performed using a multi-innovation forgetting gradient algorithm. A proportional navigation controller is developed for approaching the target and a sensor fusion estimation algorithm based on EKF is used for accurate landing.
