Partition sampling strategy for robot motion planning under uncertainty

In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage.Firstly,the environment is divided into open area and narrow area by using a partition sampling strategy,and generates the initial trajectory of the robot with fewer sampling points.Secondly,the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem,and iterates an overall optimal trajectory of robot motion.The proposed method follows the general POMDP solution framework,in which the belief dynamics is approximated by an extended Kalman filter(EKF),and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory.Using a belief space variant of iterative linear quadratic Gaussian(iLQG)to perform the value iteration,which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory.A new nominal trajectory is generated by executing the control strategy iteration,and the process is repeated until it converges to a locally optimal solution.Finally,the robot gets the optimal trajectory to safely pass through a narrow passage.The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.

Density estimation-based method to determine sample size for random sample partition of big data

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation problems.Academic research and practical applications have confirmed that RSP is an efficient solution for big data processing and analysis.However,a challenge for implementing RSP is determining an appropriate sample size for RSP data blocks.While a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data blocks.To address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data blocks.First,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)method.Second,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample size.Finally,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of DEM.Experimental results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of p.d.f.estimation.This demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

Pathway-based Analysis of the Hidden Genetic Heterogeneities in Cancers

Many cancers apparently showing similar phenotypes are actually distinct at the molecular level,leading to very different responses to the same treatment.It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers.Nevertheless,it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers.Therefore,we aimed to test this possibility in the present study.First,we used a NCI60 dataset to validate the ability of pathways to correctly partition samples.Next,we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma （DLBCL）.Finally,the clinical significance of the identified subtypes was verified using survival analysis.For the NCI60 dataset,we achieved highly accurate partitions that best fit the clinical cancer phenotypes.Subsequently,for a DLBCL dataset,we identified three hidden subtypes that showed very different 10-year overall survival rates （90％,46％ and 20％） and were highly significantly （P =0.008） correlated with the clinical survival rate.This study demonstrated that the pathwaybased approach is promising for unveiling genetic heterogeneities in complex human diseases.