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智能优化算法 神经网络预测 雷达通信 无线传感器 电力系统
信号处理 图像处理 路径规划 元胞自动机 无人机
🔥 内容介绍
路径规划是一项关键的技术，在许多领域都有广泛的应用，包括航空、航海、机器人等。在船舶运动规划中，考虑到复杂的遭遇场景，COLREG船舶运动规划算法是一种常用的方法。本文将介绍一种基于模型预测人工势场（MPAPF）的路径规划算法，用于解决COLREG船舶运动规划问题。
COLREG（国际航行规则）是指导船舶避碰行为的国际公约，旨在确保船舶在遭遇情况下能够安全地通过。COLREG规则的遵守对于保证船舶运输的安全至关重要。然而，在复杂的遭遇场景中，遵守COLREG规则并不总是容易的，因为船舶需要综合考虑多个因素，如目标船舶的运动、风、海流等。
为了解决COLREG船舶运动规划问题，研究者们提出了各种各样的算法。其中，基于模型预测人工势场（MPAPF）的方法是一种常见且有效的方法。该方法利用模型预测控制（MPC）技术，通过预测船舶运动，生成人工势场，以引导船舶的运动。MPAPF方法能够在考虑复杂的遭遇场景下，自动规划船舶的运动路径，以确保遵守COLREG规则并避免碰撞。
MPAPF算法的步骤如下：
- 收集环境信息：首先，需要获取与船舶运动相关的环境信息，包括目标船舶的位置、速度、航向等。这些信息可以通过雷达、AIS（自动识别系统）等设备获取。
- 预测船舶运动：利用模型预测控制技术，对目标船舶的未来运动进行预测。这可以通过建立动力学模型和环境模型来实现。动力学模型描述了船舶的运动规律，而环境模型描述了外部环境对船舶的影响。
- 生成人工势场：根据预测的目标船舶运动，生成人工势场。人工势场是一种虚拟的力场，用于引导船舶的运动。在MPAPF算法中，人工势场由两部分组成：静态势场和动态势场。静态势场与环境中的障碍物有关，用于避免船舶与障碍物的碰撞；动态势场与目标船舶的运动有关，用于引导船舶与目标船舶保持安全距离。
- 路径规划：根据生成的人工势场，进行路径规划。路径规划的目标是找到一条安全且符合COLREG规则的航线，以确保船舶能够安全通过复杂的遭遇场景。
- 运动控制：根据规划的航线，进行运动控制。运动控制可以通过调整船舶的航向、速度等参数来实现。在COLREG船舶运动规划中，运动控制需要考虑到COLREG规则的要求，以确保船舶的运动符合规则并避免碰撞。
通过以上步骤，基于模型预测人工势场的COLREG船舶运动规划算法能够在复杂的遭遇场景中，自动规划船舶的运动路径，以确保船舶的安全通行。该算法结合了模型预测控制技术和人工势场方法，能够有效地解决COLREG船舶运动规划问题。在实际应用中，可以根据具体情况对算法进行优化和改进，以提高路径规划的准确性和效率。
总之，路径规划是一项重要的技术，在复杂的遭遇场景下尤为关键。基于模型预测人工势场的COLREG船舶运动规划算法提供了一种有效的解决方案，能够确保船舶在遭遇情况下能够安全通过。随着技术的不断发展，路径规划算法将进一步完善和应用于更多领域，为人们的生活和工作带来更多便利和安全。
📣 部分代码
clear all, clc, close all
addpath('./code')
ver_num_reconfig_dec = 2
%% basic setting
tic
fprintf('decrease_reconfig_84_tabu.m \n')
warning('off')
addpath(pathdef)
mpopt = mpoption;
mpopt.out.all = 0; % do not print anything
mpopt.verbose = 0;
version_LODF = 0 % 1: use decrease_reconfig_algo_LODF.m
% 0: use decrease_reconfig_algo.m
distancePara = 6
candi_brch_bus = []; % candidate branch i added to bus j
casei=4
d84_v2
substation_node = 84; n_bus = 84;
combine3 = 0
n1 = 3
n2 = 2
n1_down_substation = n1+1; n2_up_ending = n2;
Branch0 = Branch;
brch_idx_in_loop0 = unique(brch_idx_in_loop(:));
%% original network's power flow (not radial)
show_biograph1 = 0
show_biograph = 0
from_to = show_biograph_not_sorted(Branch, substation_node, show_biograph1);
mpc = generate_mpc(Bus, Branch, n_bus);
res_orig = runpf(mpc, mpopt);
losses = get_losses(res_orig.baseMVA, res_orig.bus, res_orig.branch);
loss0 = sum(real(losses));
fprintf('case84_tabu: original loop network''s loss is %.5f \n\n', loss0)
% for each branch in a loop,
% if open that branch does not cause isolation, check the two ending buses
% of that branch for connectivity, realized by shortestpath or conncomp
% calculate the lowest loss increase, print out the sorted loss increase
% open the branch with lowest loss increase
% stop criterion: number of buses - number of branches = 1
%% ------------------------ Core algorithm ------------------------%%
ff0 = Branch(:, 1); ff = ff0;
tt0 = Branch(:, 2); tt = tt0;
t1 = toc;
if version_LODF
[Branch] = decrease_reconfig_algo_LODF(Bus, Branch, brch_idx_in_loop, ...
ff0, tt0, substation_node, n_bus, loss0, distancePara); %%% core algorithm
else
[Branch] = decrease_reconfig_algo(Bus, Branch, brch_idx_in_loop, ff0, tt0, ...
substation_node, n_bus, loss0); %%% core algorithm
end
t2 = toc;
time_consumption.core = t2 - t1
% output of core algorithm
from_to = show_biograph_not_sorted(Branch(:, [1 2]), substation_node, ...
show_biograph1); %%% show figure, take time
mpc = generate_mpc(Bus, Branch, n_bus);
t1 = toc;
res_pf_dec = runpf(mpc, mpopt);
t2 = toc;
time_consumption.runpf = t2-t1;
losses = get_losses(res_pf_dec.baseMVA, res_pf_dec.bus, res_pf_dec.branch);
loss0_dec = sum(real(losses)) % loss =
fprintf('case84_tabu: radial network obtained by my core algorithm''s loss is %.5f \n\n', ...
loss0_dec);
yij_dec = generate_yij_from_Branch(Branch, Branch0);
Branch_loss_record = [];
% record Branch and loss
Branch_loss_record.core.Branch = Branch;
Branch_loss_record.core.loss = loss0_dec;
%% prepare force open branches for tabu: branch_idx_focused
% nodes_focused = []
% [branch_idx_focused] = get_branch_idx_focused_for_tabu_v2( ...
% from_to, Branch0, Branch, substation_node, brch_idx_in_loop0, n_bus, ...
% n1_down_substation, n2_up_ending); % to answer reviewer 5-5's question
[branch_idx_focused] = get_branch_idx_focused_for_tabu( ...
from_to, Branch0, Branch, substation_node, brch_idx_in_loop0, n_bus, ...
n1_down_substation, n2_up_ending);
%% ------------------------ Tabu algorithm ------------------------%%
% run the core program for each upstream branch connected to the idx_force_open
for iter = 1:length(branch_idx_focused) % idx_force_open)
fprintf('iter=%d/%d\n', iter, length(branch_idx_focused)); % idx_force_open));
Branch = Branch0;
Branch(branch_idx_focused(iter), :) = [];
% % Branch(idx_force_open(iter), :) = [];
ff0 = Branch(:, 1); ff = ff0;
tt0 = Branch(:, 2); tt = tt0;
brch_idx_in_loop = brch_idx_in_loop0;
idx_tmp = find(brch_idx_in_loop == branch_idx_focused(iter));
% % idx_tmp = find(brch_idx_in_loop == idx_force_open(iter));
if isempty(idx_tmp)
else
brch_idx_in_loop(idx_tmp) = [];
brch_idx_in_loop(idx_tmp:end) = brch_idx_in_loop(idx_tmp:end)-1;
end
t1 = toc;
%%------------------- core algorithm in Tabu loop--------------------%%
if version_LODF
[Branch] = decrease_reconfig_algo_LODF(Bus, Branch, brch_idx_in_loop, ...
ff0, tt0, substation_node, n_bus, loss0, distancePara); %%% core algorithm
else
[Branch] = decrease_reconfig_algo(Bus, Branch, brch_idx_in_loop, ff0, tt0, ...
substation_node, n_bus, loss0); %%% core algorithm
end
t2 = toc;
time_consumption.tabu(iter) = t2-t1;
from_to = show_biograph_not_sorted(Branch(:, [1 2]), substation_node, ...
show_biograph); %%% show figure, take time
mpc = generate_mpc(Bus, Branch, n_bus);
t1 = toc;
res_pf = runpf(mpc, mpopt);
t2 = toc;
time_consumption.runpf(iter) = t2-t1;
losses = get_losses(res_pf.baseMVA, res_pf.bus, res_pf.branch);
lossi = sum(real(losses)) % loss = 0.2960
loss_tabu(iter,1) = lossi;
yij_dec = generate_yij_from_Branch(Branch, Branch0);
% record Branch and loss
Branch_loss_record.tabu(iter,1).Branch = Branch;
Branch_loss_record.tabu(iter,1).loss = lossi;
[PQ, PV, REF, NONE, BUS_I, BUS_TYPE, PD, QD, GS, BS, BUS_AREA, VM, ...
VA, BASE_KV, ZONE, VMAX, VMIN, LAM_P, LAM_Q, MU_VMAX, MU_VMIN] = idx_bus;
% Vm = res_pf.bus(:, VM)';
% Va = res_pf.bus(:, VA)';
% ending_bus = find_ending_node(Branch, substation_node);
% [ending_bus'; Vm(ending_bus)];
% % ending_bus = [6 14 72 22 24 83 41 42 32 33 37 55 63 8 9 10 62 71 13 82]';
% % [ending_bus'; Vm(ending_bus)]
%% ---------------------one open and one close---------------------%%
% prepare nodes_focused for one_open_one_close
t1 = toc;
[nodes_focused] = get_nodes_focused_o1c1( ...
from_to, Branch, Branch0, substation_node, brch_idx_in_loop, ...
n1_down_substation, n2_up_ending);
loss_before_switch0 = lossi;
if combine3
[record_o1c1_loss_dec, loss_after_switch_combine_two_o1c1, Branch_loss] = ...
one_open_one_close_combine3(nodes_focused, Bus, Branch0, Branch, from_to, ...
substation_node, n_bus, loss_before_switch0);
else
[record_o1c1_loss_dec, loss_after_switch_combine_two_o1c1, Branch_loss] = ...
one_open_one_close(nodes_focused, Bus, Branch0, Branch, from_to, ...
substation_node, n_bus, loss_before_switch0);
end
t2 = toc;
time_consumption.tabu_o1c1(iter) = t2-t1;
% record Branch and loss
Branch_loss_record.tabu_o1c1_dec{iter}.Branch = Branch_loss.Branch_o1c1_dec;
% Branch_loss_record.tabu_o1c1_dec(iter,1).Branch = Branch_loss.Branch_o1c1_dec;
Branch_loss_record.tabu_o1c1_dec{iter}.loss = Branch_loss.loss_o1c1_dec;
Branch_loss_record.tabu_combine_2_o1c1_dec{iter}.Branch = ...
Branch_loss.Branch_after_switch_combine_two_o1c1;
Branch_loss_record.tabu_combine_2_o1c1_dec{iter}.loss = ...
Branch_loss.loss_after_switch_combine_two_o1c1;
min_loss_o1c1 = min(record_o1c1_loss_dec(:,1));
fprintf('case84_tabu: minimum loss obtained after ''one open and one close'': %.5f\n', ...
min_loss_o1c1);
min_loss_combine_two_o1c1 = 1e9;
fprintf('case84_tabu: loss obtained after combine two ''one open and one close'': \n')
for i = 1:length(loss_after_switch_combine_two_o1c1)
temp = min(loss_after_switch_combine_two_o1c1{i});
if temp<min_loss_combine_two_o1c1
min_loss_combine_two_o1c1 = temp;
end
fprintf(' %.5f \n', temp);
end
fprintf('case84_tabu: minimum loss obtained after combine two ''one open and one close'': %.5f \n', ...
min_loss_combine_two_o1c1)
%% ---------------------two open and two close---------------------%%
flag_2o2c = 0
if flag_2o2c == 1
t1 = toc;
loss_before_switch0 = lossi;
[record_o2c2_loss_dec, loss_after_switch_combine_two_o2c2] = ...
two_open_two_close(nodes_focused, Bus, Branch0, Branch, from_to, ...
substation_node, n_bus, loss_before_switch0);
t2 = toc;
time_consumption.tabu_o2c2(iter) = t2-t1;
min_loss_o2c2 = min(record_o2c2_loss_dec(:,1));
fprintf('case84_tabu: minimum loss obtained after ''two open and two close'': %.5f\n', ...
min_loss_o2c2);
min_loss_combine_two_o2c2 = 1e9;
fprintf('case84_tabu: loss obtained after combine two ''two open and two close'': \n')
for i = 1:length(loss_after_switch_combine_two_o2c2)
temp = min(loss_after_switch_combine_two_o2c2{i});
if temp<min_loss_combine_two_o2c2
min_loss_combine_two_o2c2 = temp;
end
fprintf(' %.5f \n', temp);
end
fprintf('case84_tabu: minimum loss obtained after combine two ''two open and two close'': %.5f \n', ...
min_loss_combine_two_o2c2)
res_save{iter}.min_loss_o2c2 = min_loss_o2c2;
res_save{iter}.min_loss_combine_two_o2c2 = min_loss_combine_two_o2c2;
end
res_save{iter}.yij_dec = yij_dec;
res_save{iter}.Branch = Branch;
res_save{iter}.lossi = lossi;
res_save{iter}.record_o1c1_loss_dec = record_o1c1_loss_dec;
res_save{iter}.min_loss_o1c1 = min_loss_o1c1;
res_save{iter}.min_loss_combine_two_o1c1 = min_loss_combine_two_o1c1;
% save(file_name, 'yij_dec', 'Branch', 'lossi');
file_name = ['id', num2str(ver_num_reconfig_dec), ...
'_case84_yij_Branch', '.mat'];
save(file_name, 'res_save', 'branch_idx_focused', 'Branch_loss_record', ...
'time_consumption');
end
file_name = ['id', num2str(ver_num_reconfig_dec), ...
'_case84_yij_Branch', '.mat'];
save(file_name, 'res_save', 'branch_idx_focused', 'Branch_loss_record', ...
'time_consumption');
% find_all_losses(Branch_loss_record);
fprintf('case84_tabu: losses obtained after applying tabu strategy: \n') % 0.28343 zjp 2018-1-18
fprintf('%.5f \n', loss_tabu)
fprintf('----- min: %.5f -----\n', min(loss_tabu))
min_loss = 1e9;
for i = 1:length(res_save)
if min_loss>res_save{i}.min_loss_o1c1
min_loss = res_save{i}.min_loss_o1c1 ;
end
if min_loss>res_save{i}.min_loss_combine_two_o1c1
min_loss = res_save{i}.min_loss_combine_two_o1c1 ;
end
end
min_loss_o1c1 = min_loss
if flag_2o2c == 1
min_loss = 1e9;
for i = 1:length(res_save)
if min_loss>res_save{i}.min_loss_o2c2
min_loss = res_save{i}.min_loss_o2c2 ;
end
if min_loss>res_save{i}.min_loss_combine_two_o2c2
min_loss = res_save{i}.min_loss_combine_two_o2c2 ;
end
end
min_loss_o2c2 = min_loss
end
fprintf('\n')
ver_num_reconfig_dec⛳️ 运行结果
编辑