34 research outputs found
Smooth quasi-developable surfaces bounded by smooth curves
Computing a quasi-developable strip surface bounded by design curves finds
wide industrial applications. Existing methods compute discrete surfaces
composed of developable lines connecting sampling points on input curves which
are not adequate for generating smooth quasi-developable surfaces. We propose
the first method which is capable of exploring the full solution space of
continuous input curves to compute a smooth quasi-developable ruled surface
with as large developability as possible. The resulting surface is exactly
bounded by the input smooth curves and is guaranteed to have no
self-intersections. The main contribution is a variational approach to compute
a continuous mapping of parameters of input curves by minimizing a function
evaluating surface developability. Moreover, we also present an algorithm to
represent a resulting surface as a B-spline surface when input curves are
B-spline curves.Comment: 18 page
Fact-based Agent modeling for Multi-Agent Reinforcement Learning
In multi-agent systems, agents need to interact and collaborate with other
agents in environments. Agent modeling is crucial to facilitate agent
interactions and make adaptive cooperation strategies. However, it is
challenging for agents to model the beliefs, behaviors, and intentions of other
agents in non-stationary environment where all agent policies are learned
simultaneously. In addition, the existing methods realize agent modeling
through behavior cloning which assume that the local information of other
agents can be accessed during execution or training. However, this assumption
is infeasible in unknown scenarios characterized by unknown agents, such as
competition teams, unreliable communication and federated learning due to
privacy concerns. To eliminate this assumption and achieve agent modeling in
unknown scenarios, Fact-based Agent modeling (FAM) method is proposed in which
fact-based belief inference (FBI) network models other agents in partially
observable environment only based on its local information. The reward and
observation obtained by agents after taking actions are called facts, and FAM
uses facts as reconstruction target to learn the policy representation of other
agents through a variational autoencoder. We evaluate FAM on various Multiagent
Particle Environment (MPE) and compare the results with several
state-of-the-art MARL algorithms. Experimental results show that compared with
baseline methods, FAM can effectively improve the efficiency of agent policy
learning by making adaptive cooperation strategies in multi-agent reinforcement
learning tasks, while achieving higher returns in complex
competitive-cooperative mixed scenarios
Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization
The implementation of low-energy cooperative movements is one of the key technologies for the complex control of the movements of humanoid robots. A control method based on optimal parameters is adopted to optimize the energy consumption of the cooperative movements of two humanoid robots. A dynamic model that satisfies the cooperative movements is established, and the motion trajectory of two humanoid robots in the process of cooperative manipulation of objects is planned. By adopting the control method with optimal parameters, the parameters optimization of the energy consumption index function is performed and the stability judgment index of the robot in the movement process is satisfied. Finally, the effectiveness of the method is verified by simulations and experimentations
Preliminary Study:Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients