网格城市通拥堵或其他紧急情况

考虑在某个网格城市中，由于交通拥堵或其他紧急情况，往往导致救护车无法及时抵达现场。因此，急救中心计划开展无人机急救项目，该系统可以在紧急情况下传送急救物资。已知城市的地图是一个的网格，无人机必须在网格间穿行。已知各网格建筑物高度（见附件），规定建筑超过10米无人机需要绕行。
以网格左上角点为原点建立坐标系，已知急救中心位置为1 1，需运送物资的位置为50 80 80 50 110 50 150 20 160 20，构建一个数学模型找到无人机从急救中心出发将急救物质传送到各需求点最后回到医院的最优路径。

化】无人机路径规划的几种算法总结-无人机路径规划是无人机应用领域的重要问题，其涉及到无人机的航迹规划、避障、任务分配等多方面的问题。常用的无人机路径规划算法包括A算法、D算法、RRT算法、遗传算法等。在实际应用中，需要根据具体情况选择合适的算法进行路径规划，同时还需要考虑系统实时性、安全性、稳定性等因素。

参考代码: 由于本人并非matlab专家，无法提供完整可行的matlab代码。建议查阅相关文献和资料结合实际情况选择合适的算法进行路径规划。以下是部分可供参考的matlab代码片段：

  1. A*算法：
% A star path planning algorithm
function [chess,path] = A_star(chess, start, goal)
[path,CLOSED] = Asearch(start,goal,chess);% A star search
if isempty(path)   % No path found
    ERROR('No Path Found!');
end
end

function [path,CLOSED] = Asearch(start,goal,grid)
% A*
% Determine dimensions of input
[nrows,ncols] = size(grid);

% Define the node class
classdef Node < handle
    properties
        id = [];
        x = [];
        y = [];
        g = [];
        h = [];
        parent = [];
    end
end

% Initialize the open and closed list
OPEN = Node.empty;
OPEN_ID_List = [];
CLOSED = Node.empty;
path = [];
path_cost = inf;

% Define the initial node and add it to the open list
start_node = Node;
start_node.id = tuple(start(1),start(2));
start_node.x = start(1);
start_node.y = start(2);
start_node.g = 0;
start_node.h = H_func(start_node,goal);
start_node.parent = [];
OPEN = [OPEN,start_node];
OPEN_ID_List = [OPEN_ID_List;unique(start_node.id)];


end
  1. 遗传算法：
% Use the genetic algorithm to optimize the path of the UAV
% Parameters
numGenerations = 150;
popSize = 100;
mutationRate = 0.01;
crossoverRate = 0.8;
eliteCount = 1;

% Initialization
pop = createPopulation(popSize,start,goal,obstacleList,cellSize);

% For every generation
for i = 1:numGenerations
    % Evaluate the population
    fitness = evaluatePopulation(pop,goal);

    % Select the elite individuals
    elite = selectElite(pop,fitness,eliteCount);

    % Apply the crossover operator to create the offspring
    offspring = crossover(pop,fitness,crossoverRate,popSize);

    % Apply the mutation operator to introduce new individuals
    offspring = mutate(offspring,mutationRate);

    % Create the new population from the elite and offspring
    pop = [elite,offspring];
end

% Find the best individual
bestIndividual = selectBest(pop,goal);

% Extract the path from the best individual
path = extractPath(bestIndividual);