【PRM路径规划】基于PRM采样结合粒子群和蚁群算法实现机器人障碍路径规划附matlab代码

%% ABC + PSO Path Planning Problem % Here, system tries to find the most optimal path between starting point % and destination point with aid of Artificial Bee Colony (ABC) algorithm % and Particle Swarm Optimization algorithm combined. It is good strategy % in robotics path finding. In each run new obstacles in new positions % defines and a curved line tries to find the best path. Run multiple times % to find the best result. % Hope this code help you :) %% Cleaning The Stage clc; clear; warning(’off’); %% Start ABC + PSO Optimal Path Finder model=Basics(); model.n=6; % number of Handle Points CostFunction=@(x) Cost(x,model); % Cost Function nVar=model.n; % Number of Decision Variables VarSize=[1 nVar]; % Size of Decision Variables Matrix VarMin.x=model.xmin; % Lower Bound of Variables VarMax.x=model.xmax; % Upper Bound of Variables VarMin.y=model.ymin; % Lower Bound of Variables VarMax.y=model.ymax; % Upper Bound of Variables %% PSO + ABC Parameters MaxIt=150; % Maximum Number of Iterations nPop=20; % Population Size (Swarm Size) w=1; % Inertia Weight wdamp=0.98; % Inertia Weight Damping Ratio c1=1.5; % Personal Learning Coefficient end end % Update Best Solution Ever Found for i = 1:nPop if pop(i).Cost <= BestSol.Cost BestSol = pop(i); end end tOfTheRange)=-particle(i).Velocity.y(OutOfTheRange); % Update Position Bounds particle(i).Position.y = max(particle(i).Position.y,VarMin.y); particle(i).Position.y = min(particle(i).Position.y,VarMax.y); % Flag=[’, Violation = ’ num2str(GlobalBest.Sol.Violation)]; end disp([’In Iteration Number ’ num2str(it) ’:ABC+PSO Fittest Value Is = ’ num2str(BestCost(it)) Flag]); % Plot Solution figure(1); Plotting(GlobalBest.Sol,model); pause(0.01); end %% Plot Train Stage figure; plot(sort(BestCost,’descend’),’r.’,’LineWidth’,3); title (’ABC + PSO Training’); xlabel(’Itr’); ylabel(’ABC+PSO Fittest Value’); grid on;