Safe and effective navigation of autonomous robots in hazardous environments

The development of autonomous mobile machines to perform useful tasks in real work environments is currently being impeded by concerns over effectiveness, commercial viability and, above all, safety. This paper introduces a case study of a robotic excavator to explore a series of issues around system development, navigation in unstructured environments, autonomous decision making and changing the behaviour of autonomous machines to suit the prevailing demands of users. The adoption of the Real-Time Control Systems (RCS) architecture (Albus, 1991) is proposed as a universal framework for the development of intelligent systems. In addition it is explained how the use of Partially Observable Markov Decision Processes (POMDP) (Kaelbling et al., 1998) can form the basis of decision making in the face of uncertainty and how the technique can be effectively incorporated into the RCS architecture. Particular emphasis is placed on ensuring that the resulting behaviour is both task effective and adequately safe, and it is recognised that these two objectives may be in opposition and that the desired relative balance between them may change. The concept of an autonomous system having “values” is introduced through the use of utility theory. Limited simulation results of experiments are reported which demonstrate that these techniques can create intelligent systems capable of modifying their behaviour to exhibit either ‘safety conscious’ or ‘task achieving’ personalities.     

基于QS-ML-PMHT的多目标无源协同定位方法

针对目标个数未知时无源协同定位系统低可观测目标的航迹起始及维持问题,提出一种基于拟蒙特卡罗模拟退火极大似然概率多假设的双基站多目标无源协同定位方法。首先,建立双基站无源协同定位系统数学模型。其次,提出基于极大似然概率多假设的多目标无源协同定位航迹起始算法,通过假设法确定目标个数,并首次利用拟蒙特卡罗模拟退火算法解决极大似然概率多假设中多目标的优化求解问题,以提高多目标检测跟踪性能。最后,通过滑窗法实现航迹维持。仿真结果表明,所提方法能够有效解决目标个数未知时双基站无源协同定位系统低可观测目标的航迹起始及维持问题。     

基于观测的POMDP 优化算法及其仿真

在分析马尔可夫决策过程（Markov Decision Process, MDP）性能灵敏度的基础上,讨论了部分可观 测马尔可夫决策过程（Partially Observable Markov Decision Process, POMDP）的性能优化问题．给出了POMDP 性能灵敏度分析公式,并以此为基础提出了两种基于观测的POMDP 优化算法：策略梯度优化算法和策略迭 代优化算法．最后以准许控制问题为仿真实例,验证了这两个算法的有效性．     

基于部分可观察马尔可夫决策过程的受控无线网络系统动态资源分配

研究了受控无线网络的动态资源分配。针对传统无线通信传输模型的局限性随着无线通信系统架构的发展日益凸显的问题,提出了一种引入反馈控制策略的受控无线网络模型。该模型结合部分可观察马尔可夫决策过程(POMDP),将用户接收功率与数据传输误码率作为反馈观测对象,对通信小区内基站天线开启数与用户接入数进行动态资源最优匹配。仿真结果表明,这种方法能够有效提升系统传输能效性与可靠性,降低传输误码率,改善系统资源动态匹配控制性能。     

A general formulation of monotonically convergent algorithms in the control of quantum dynamics beyond the linear dipole interaction

This paper presents a general method to formulate monotonically convergent algorithms for identifying optimal control fields to manipulate quantum dynamics phenomena beyond the linear dipole interaction. The method, facilitated by a field-dependent dipole moment operator, is based on an integral equation of the first kind arising from the Heisenberg equation of motion for tracking a time-dependent, dynamical invariant observable associated with a reference control field.     

一类不确定相似广义互联系统的广义状态重构

研究了一类具有相似结构的广义互联系统 ,根据系统的相似结构构造了一个动态组合系统 ,并以组合系统的可测输出为基础来实现广义互联系统中各个子系统的广义状态重构 ,从而提出了相似性研究的新方向 .这类系统的各个孤立子系统都包含非线性项 ,而且子系统之间的互联项都是非线性非匹配不确定的 ,研究表明 ,广义互联系统的相似结构可以简化其子系统的广义状态重构     

A note on observables on MV-algebras

 A criterion when a subset of an MV-algebra is contained in the range of a (finitely additive) observable is given. It is used to prove that every at most countable subset of any MV-algebra is contained in the range of an observable. A much stronger result holds for any bold fuzzy algebra [0,1] ^S , which is whole contained in the range of a (σ-additive) observable.     

基于FPOMDP的无线传感器网络动态调度算法

针对无线传感器网络节点能量有限、数据采集易受环境影响的问题,提出一种基于可分解部分可观察Markov决策过程FPOMDP( Factored Partially Observable Markov Decision Process )的节点休眠调度算法.通过节点空时相关模型求取休眠节点数据,利用网络数据准确性和节点能量间的条件独立关系,构造状态转移函数、观察函数和奖赏函数,采用值迭代求解算法求取最优策略,实现节点动态调度.仿真结果表明,该算法能够在保证数据准确性的前提下,有效降低节点能量消耗,延长网络生存时间.     

基于信念重用的WSNs能量高效跟踪

针对无线传感器网络(WSNs)中目标跟踪性能与传感器能量消耗难以平衡问题,提出一种信念重用的WSNs能量高效跟踪算法。使用部分可观察马尔可夫决策过程(POMDPs)对动态不确定环境下的WSNs进行建模,将跟踪性能与能量消耗平衡优化问题转化为POMDPs最优值函数求解过程;采用最大报酬值启发式查找方法获得跟踪性能的逼近最优值;采用信念重用方法避免重复获取信念,有效降低传感器通信带来的能量消耗。实验结果表明:信念重用算法能够有效优化跟踪性能与能量消耗之间的平衡,达到以较低的能量消耗获得较高跟踪性能的目的。