The fluctuation in system efficiency caused by coil misalignment in wireless power transfer has drawn the attention of researchers in the ***, this paper proposes a method to obtain coil misalignment. By utilizing the...
The fluctuation in system efficiency caused by coil misalignment in wireless power transfer has drawn the attention of researchers in the ***, this paper proposes a method to obtain coil misalignment. By utilizing the acquired coil position information, anti-misalignment correction can be applied to enhance system efficiency. Firstly, two auxiliary coils are added at the receiving end. Then, the misalignment between the X and Y axes is converted into polar coordinates. The output current and coil position misalignment angle are used as inputs to establish a multiple linear regression model for identifying the receiving end's position information. Finally, a random motion trajectory is set and simulated in the system to validate the feasibility of the proposed model.
This paper studies the consensus problem in multi-agent systems (MASs) under the challenge of an unknown system model and limited communication resources. A novel model-free adaptive learning algorithm is developed to...
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We propose a robust framework for planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares ke...
We propose a robust framework for planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated in rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity property of the angle representation, our framework requires only a linear solver to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC- LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.
High-precision maps and localization techniques are important modules in robot systems. However, the existing simultaneous localization and mapping methods using the same sensors in both localization and mapping tasks...
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A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in comb...
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This article proposes an adaptive and robust terrain classification control algorithm for a pendulum-driven spherical robot, aiming to solve the problem of insufficient control accuracy caused by using the same contro...
This article proposes an adaptive and robust terrain classification control algorithm for a pendulum-driven spherical robot, aiming to solve the problem of insufficient control accuracy caused by using the same controller for different terrains. The common terrains are classified into three categories, and a terrain classification dataset is established based on the vibration signal of the robot. Using LightGBM, combined with the feature window and window voter algorithm proposed in this article, the terrain classification results are corresponded with three proposed controllers. Physical experiment results show that the proposed classification control algorithm can work stably in different terrains, guiding the spherical robot to select the optimal controller to improve its motion performance.
Human-robot motion retargeting is a crucial approach for fast learning motion skills. Achieving real-time retargeting demands high levels of synchronization and accuracy. Even though existing retargeting methods have ...
Human-robot motion retargeting is a crucial approach for fast learning motion skills. Achieving real-time retargeting demands high levels of synchronization and accuracy. Even though existing retargeting methods have swift calculation, they still cause time-delay effect on the synchronous retargeting. To mitigate this issue, this paper proposes a motion retargeting method guided by prediction, which effectively reduces the adverse impact of time-delay. The proposed pipeline contains motion retargeting in spatial-temporal graph-based structure and motion prediction in the latent space. The motion sequence retargeting builds mapping and paired data from human poses to corresponding robot configurations for training prediction model, and generated robot motion satisfies limit and self-collision constrains. The controller guided by prediction imports future robot joint motion to achieve advanced trajectory tracking, thereby compensating for delay time spent on calculation and tracking. Experimental results show that our method outperforms other methods in terms of synchronization and similarity. Furthermore, our method exhibits fault-tolerant capability in scenarios involving the loss of human information input.
Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the targe...
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Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (...
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LiDAR has become a widely used sensor in many autonomous areas, and LiDAR SLAM is one of essential applications. With the tendency of lightweight structure and the improvement of LiDAR resolution, downsampling points ...
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