28 research outputs found
Forward Kinematics of Object Transport by a Multi-Robot System with Deformable Sheet
We present object handling and transport by a multi-robot team with a
deformable sheet as a carrier. Due to the deformability of the sheet and the
high dimension of the whole system, it is challenging to clearly describe all
the possible positions of the object on the sheet for a given formation of the
multi-robot system. A complete forward kinematics (FK) method is proposed in
this paper for object handling by an -mobile robot team with a deformable
sheet. Based on the virtual variable cables model, a constrained quadratic
problem (CQP) is formulated by combining the form closure and minimum potential
energy conditions of the system. Analytical solutions to the CQP are presented
and then further verified with the force closure condition. With the proposed
FK method, all possible solutions are obtained with the given initial sheet
shape and the robot team formation. We demonstrate the effectiveness,
completeness, and efficiency of the FK method with simulation and experimental
results.Comment: 8 pages, 6 figures, has been submitted to IEEE Robotics and
Automation Letter
Multi-Robot Object Transport Motion Planning with a Deformable Sheet
Using a deformable sheet to handle objects is convenient and found in many
practical applications. For object manipulation through a deformable sheet that
is held by multiple mobile robots, it is a challenging task to model the
object-sheet interactions. We present a computational model and algorithm to
capture the object position on the deformable sheet with changing robotic team
formations. A virtual variable cables model (VVCM) is proposed to simplify the
modeling of the robot-sheet-object system. With the VVCM, we further present a
motion planner for the robotic team to transport the object in a
three-dimensional (3D) cluttered environment. Simulation and experimental
results with different robot team sizes show the effectiveness and versatility
of the proposed VVCM. We also compare and demonstrate the planning results to
avoid the obstacle in 3D space with the other benchmark planner.Comment: 8 pages, 10 figures, accepted by RAL&CASE 2022 in June 24, 202
Improving Multi-Person Pose Tracking with A Confidence Network
Human pose estimation and tracking are fundamental tasks for understanding
human behaviors in videos. Existing top-down framework-based methods usually
perform three-stage tasks: human detection, pose estimation and tracking.
Although promising results have been achieved, these methods rely heavily on
high-performance detectors and may fail to track persons who are occluded or
miss-detected. To overcome these problems, in this paper, we develop a novel
keypoint confidence network and a tracking pipeline to improve human detection
and pose estimation in top-down approaches. Specifically, the keypoint
confidence network is designed to determine whether each keypoint is occluded,
and it is incorporated into the pose estimation module. In the tracking
pipeline, we propose the Bbox-revision module to reduce missing detection and
the ID-retrieve module to correct lost trajectories, improving the performance
of the detection stage. Experimental results show that our approach is
universal in human detection and pose estimation, achieving state-of-the-art
performance on both PoseTrack 2017 and 2018 datasets.Comment: Accepted by IEEE Transactions on Multimedia. 11 pages, 5 figure
FHT-Map: Feature-based Hierarchical Topological Map for Relocalization and Path Planning
Topological maps are favorable for their small storage compared to geometric
map. However, they are limited in relocalization and path planning
capabilities. To solve this problem, a feature-based hierarchical topological
map (FHT-Map) is proposed along with a real-time map construction algorithm for
robot exploration. Specifically, the FHT-Map utilizes both RGB cameras and
LiDAR information and consists of two types of nodes: main node and support
node. Main nodes will store visual information compressed by convolutional
neural network and local laser scan data to enhance subsequent relocalization
capability. Support nodes retain a minimal amount of data to ensure storage
efficiency while facilitating path planning. After map construction with robot
exploration, the FHT-Map can be used by other robots for relocalization and
path planning. Experiments are conducted in Gazebo simulator, and the results
demonstrate that the proposed FHT-Map can effectively improve relocalization
and path planning capability compared with other topological maps. Moreover,
experiments on hierarchical architecture are implemented to show the necessity
of two types of nodes.Comment: 8 pages, 7figures, 2 table
A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints
Multi-mobile robot systems show great advantages over one single robot in
many applications. However, the robots are required to form desired
task-specified formations, making feasible motions decrease significantly.
Thus, it is challenging to determine whether the robots can pass through an
obstructed environment under formation constraints, especially in an
obstacle-rich environment. Furthermore, is there an optimal path for the
robots? To deal with the two problems, a novel graphbased motion planner is
proposed in this paper. A mapping between workspace and configuration space of
multi-mobile robot systems is first built, where valid configurations can be
acquired to satisfy both formation constraints and collision avoidance. Then,
an undirected graph is generated by verifying connectivity between valid
configurations. The breadth-first search method is employed to answer the
question of whether there is a feasible path on the graph. Finally, an optimal
path will be planned on the updated graph, considering the cost of path length
and formation preference. Simulation results show that the planner can be
applied to get optimal motions of robots under formation constraints in
obstacle-rich environments. Additionally, different constraints are considered
Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm
Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial.Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design.Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets.Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
Recently, the success of pre-training in text domain has been fully extended
to vision, audio, and cross-modal scenarios. The proposed pre-training models
of different modalities are showing a rising trend of homogeneity in their
model structures, which brings the opportunity to implement different
pre-training models within a uniform framework. In this paper, we present
TencentPretrain, a toolkit supporting pre-training models of different
modalities. The core feature of TencentPretrain is the modular design. The
toolkit uniformly divides pre-training models into 5 components: embedding,
encoder, target embedding, decoder, and target. As almost all of common modules
are provided in each component, users can choose the desired modules from
different components to build a complete pre-training model. The modular design
enables users to efficiently reproduce existing pre-training models or build
brand-new one. We test the toolkit on text, vision, and audio benchmarks and
show that it can match the performance of the original implementations