Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing meth...
Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration.
Tissue sections can reveal tumour-specific changes, and a tumour diagnosis can be made by analyzing the arrangement and distribution of cells on the surface. Although histopathological diagnosis based on tissue sectio...
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This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is *** problem is formula...
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This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is *** problem is formulated as a variant of the orienteering *** on the genetic algorithm(GA),we propose the greedy strategy based GA(GGA)which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization,and use a differential evolution planner for providing the deterministic local path *** uncertainty of the local path cost comes from unpredictable obstacles,measurement error,and trajectory tracking *** improve the robustness of the planner in an uncertain environment,a sampling strategy for path evaluation is designed,and the cost of a certain route is obtained by multiple sampling from the probability density functions of local *** Carlo simulations are used to verify the superiority and effectiveness of the *** promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6%in terms of total profit,and the sampling-based GGA route planner(S-GGARP)improves the average profit by 5.5%compared to the GGA route planner(GGARP).
A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in thi...
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A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in this paper. By regarding the control input and the multiplicative actuator failure as two players of a zero-sum game, a performance index function reflecting the trajectory tracking error, the control policy and the unknown failure is established. To handle the tracking Hamilton-Jacobi-Isaacs (HJI) equation associated with the established performance index function, a critic neural network is utilized. Then, the Nash equilibrium solution can be obtained, and the FTTC is achieved. The stability of the faulty nonlinear system is examined through Lyapunov-based approach. Finally, simulation results of a spring system is provided to ensure the utility and the realizability of the ZSDG-based FTTC approach.
In order to obtain a more anthropomorphic experience in remote control, we use a wearable exoskeleton type master hand, which can obtain real-time human upper limb motion information. And through the master-slave cons...
In order to obtain a more anthropomorphic experience in remote control, we use a wearable exoskeleton type master hand, which can obtain real-time human upper limb motion information. And through the master-slave constraint algorithm, it completes the synchronous control of the slave robot, and then completes a series of tasks that people cannot directly participate in. The original control scheme is further optimized, so that the tracking accuracy of the slave end effector is significantly improved. First, the Denavit-Hartenberg model of the master and slave devices is established through Robotic Toolbox 9.10 robot toolbox, and the workspace of the master and slave devices is analyzed respectively using Monte Carlo method. Secondly, the control scheme is designed to make the master exoskeleton complete the high-precision real-time control of the slave robot. Finally, the simulation results show that the master exoskeleton can complete the synchronization control of the slave robot, and the terminal accuracy is further improved on the basis of the original algorithm.
With the development of shared control technology for humanoid prosthetic hands, more and more research is focused on vision-based machine decision making. In this paper, we propose a miniaturized eye-in-hand target o...
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ISBN:
(纸本)9781665481106
With the development of shared control technology for humanoid prosthetic hands, more and more research is focused on vision-based machine decision making. In this paper, we propose a miniaturized eye-in-hand target object prediction and action decision-making framework for the humanoid hand “approach-grasp” sequence. Our prediction system can simultaneously predict the target object and detect temporal localization of the grasp action. The system is divided into three main modules: feature logging, target filtering and grasp triggering. In this paper, the optimal configuration of the hyper-parameters designed in each module is performed experimentally. We also propose a prediction quality assessment method for “approach-grasp” behavior, including instance level, sequence level and action decision level. With the optimal hyper-parameter configuration, the predicting system perform averagely to 0.854 at instance prediction accuracy (IP), 0.643 at grasp action prediction accuracy (GP). It also has good predictive stability for most classes of objects with number of predicting changes (NPC) below 6.
In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measure...
In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measurements. The lateral acceleration constraint is further added to trajectory estimation to make the estimated trajectory follow the motion characteristics of vehicles. In addition, since vehicle model parameters vary with different motion conditions and tyre pressure, we estimate vehicle correction factors that rectify changes in vehicle model parameters online, and also analyze the observability of these vehicle correction factors. In experiments, the proposed method is evaluated and compared with state-of-the-art methods in the public dataset. The experimental results show that the proposed method achieves more accurate results in all sequences since we add additional sensor measurements and utilize the characteristic of vehicle motion to restrict the trajectory estimation. The ablation study also proved the effectiveness of continuous-time representation, online correction factor estimation, and incorporation of lateral acceleration constraint.
Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it i...
Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.
Since the sensors of spherical robots rotate with the longitudinal axis, an efficient velocity controller must also account for robot's attitude. Therefore, this paper proposed an optimal velocity controller for s...
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ISBN:
(纸本)9781665481106
Since the sensors of spherical robots rotate with the longitudinal axis, an efficient velocity controller must also account for robot's attitude. Therefore, this paper proposed an optimal velocity controller for spherical robots based on the offset-free linear model predictive control (LMPC), which controls the velocity while considering robot's attitude, motor's current and other variables. The prediction model of LMPC was generated by linearizing and discretizing the dynamic model and introducing the disturbance model. Then the kalman filter (KF) was used to estimate the states and disturbance. After that, combined with LMPC, the offset-free tracking of different velocities under different terrains was achieved by fully considering velocity, attitude and current in the target function. Finally, a series of physical experiments show that the proposed controller ensures fast convergence, strong stability and no bias, while the velocity changes more smoothly, the attitude changes less and the current output is more stable.
With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of ...
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