基于路径评价模型的自适应轨迹跟踪控制

针对现有移动机器人轨迹跟踪控制主要考虑自身位姿误差,未考虑路径曲率对跟踪控制的影响,为了进一步提高智能移动放线机器人的跟踪精度,提出了一种基于路径评价模型的控制方法(trajectory evaluation model controller,TEMC)。建立移动机器人运动学相关模型;为了描述参考路径与机器人之间的几何关系建立了路径评价模型(trajectory evaluation model,TEM),并引入曲率及曲率变化率对路径复杂度进行定义,同时综合路径复杂度与位姿误差因素设计了路径评价函数;借助BP神经网络,提出了一种基于路径评价模型的控制器,并给出了稳定性证明;通过仿真实验证明了路径评价模型的有效性,并通过实验法给出了路径评价模型中核心参数的取值范围,同时TEMC跟踪精度相较于传统自适应反演控制器提升了48%以上。

An Algorithm to Find K Shortest Path

In this figure, it finds a vertex to another vertex k shortest path algorithm. Provided there are n vertices and edges in the diagram. If the path loops, the time complexity of the algorithm is allowed O（w ＋ n log 2 n ＋ kw log 2 k）. If the request path does not contain the loop, the time complexity of the algorithm O（kn（w ＋ n log2 n）＋ kw log2 k）. The algorithm utilizes a simple extension of the Dijkstra algorithm determined the end of the length of the shortest path to the other vertices, and then, based on these data, branch and bound method to identify the required path. Experimental results show that the actual running time has relations with the structure of FIG.

Layered heuristic algorithm for multiple restriction routes

A layered algorithm by bidirectional searching is proposed in this paper to solve the problem that it is difficult and time consuming to reach an optimal solution of the route search with multiple parameter restrictions for good quality of service. Firstly, a set of reachable paths to each intermediate node from the source node and the sink node based on adjacent matrix transformation are calculated respectively. Then a temporal optimal path is selected by adopting the proposed heuristic method according to a non-linear cost function. When the total number of the accumulated nodes by bidirectional searching reaches n-2, the paths from two directions to an intermediate node should be combined and several paths via different nodes from the source node to the sink node can be obtained, then an optimal path in the whole set of paths can be taken as the output route. Some simulation examples are included to show the effectiveness and efficiency of the proposed method. In addition, the proposed algorithm can be implemented with parallel computation and thus, the new algorithm has better performance in time complexity than other algorithms. Mathematical analysis indicates that the maximum complexity in time, based on parallel computation, is the same as the polynomial complexity of O(kn2-3kn+k), and some simulation results are shown to support this analysis.

On Models of Nonlinear Evolution Paths in Adiabatic Quantum Algorithms

In this paper,we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state.If the overlap between the initial state and final state of the quantum system is not equal to zero,both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity".But when the initial state has a zero overlap with the solution state in the problem,the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time.However,inspired by a related reference,a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model - an increase in energy.Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above.These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems.