✅作者简介热爱科研的Matlab仿真开发者擅长毕业设计辅导、数学建模、数据处理、建模仿真、程序设计、完整代码获取、论文复现及科研仿真。 往期回顾关注个人主页Matlab科研工作室 关注我领取海量matlab电子书和数学建模资料个人信条格物致知,完整Matlab代码获取及仿真咨询内容私信。 内容介绍在当今智能化发展迅速的时代智能仓库作为物流行业的关键环节多机器人系统的应用愈发广泛。然而机器人电池电量对任务执行效率和系统整体性能有着重要影响。电池感知分层任务分配策略能有效解决这一问题提升智能仓库多机器人系统的运行效能。多机器人系统在智能仓库中的应用现状智能仓库的工作流程智能仓库涵盖货物存储、检索、分拣等多个环节。多机器人协同工作部分机器人负责搬运货物从货架取下并运输至指定地点部分负责库存盘点扫描货物信息以更新库存数据。现有任务分配方式的局限传统任务分配常基于任务优先级或距离等单一因素。例如仅将距离货物近的机器人分配任务未充分考虑机器人电池电量。这可能导致电量低的机器人频繁执行任务提前耗尽电量影响整体工作进度。同时未对任务进行分层管理复杂任务和简单任务同等分配无法充分发挥不同机器人的优势。电池感知分层任务分配策略解析电池感知机制电量监测机器人配备高精度电量传感器实时监测电池电量并将电量信息反馈至中央控制系统。这为任务分配提供关键数据基础。电量阈值设定依据机器人工作时长、任务功耗等因素设定不同电量阈值。如高电量阈值为 80%当机器人电量高于此值可执行复杂、耗时任务低电量阈值设为 20%低于此值则需优先充电。分层任务体系构建任务分类将智能仓库任务分为三个层次。高层任务如货物的战略布局调整对机器人决策能力和信息处理能力要求高中层任务包括常规货物搬运需一定运动和操作能力低层任务如货架清洁相对简单对机器人要求较低。任务优先级确定高层任务因关乎仓库整体运营策略优先级最高中层任务影响货物流转效率次之低层任务优先级最低。分层任务分配流程高层任务分配中央控制系统先筛选电量充足且具备高级决策能力的机器人执行高层任务。系统分析任务需求与机器人状态优先分配给电量高于 80% 且处理能力强的机器人。中层任务分配在满足高层任务分配后为中层任务分配机器人。此时电量在 50% - 80% 的机器人被优先考虑确保任务高效执行的同时合理利用电量。低层任务分配最后分配低层任务电量相对较低但仍能完成简单任务的机器人被安排执行。若机器人电量低于 20%则优先分配充电任务。电池感知分层任务分配的优势提升任务执行效率根据机器人电量和能力分层分配任务避免电量低的机器人承担复杂任务减少任务中断次数。例如电量足的机器人执行高层任务快速完成货物布局调整为后续中层搬运任务提供良好规划提高整体任务流转速度。延长机器人使用寿命避免机器人过度放电或在电量低时执行高负荷任务减少电池损耗。合理的任务分配使机器人电池使用更科学延长电池更换周期降低维护成本。增强系统稳定性当部分机器人因电量问题退出任务时中央控制系统可依据电池感知和分层任务分配策略及时调整任务分配由其他合适机器人接替确保智能仓库运营不受较大影响。⛳️ 运行结果 部分代码clcclear all​%Initial robot Locationsbot_locs zeros(25,2);​b1;b_dash 0;while(b20 b_dash9)bot_locs(b,:) [78*b_dash, 2];bot_locs(b1,:) [78*b_dash, 4];b b2;b_dash b_dash 1;endb_dash 0;while(b25 b_dash4)bot_locs(b,:) [275*b_dash, 6];b b1;b_dash b_dash 1;end​%Number of tasks done by each robot: normal and chargingTasks zeros(25,2);​%Initializing Final Task Locations of all robotsfinal_task_locs zeros(25,2);​%Initializing Initial Battery Level of all robotsbattery 100*ones(size(bot_locs,1),1);​%Number of tasks per order set, assuming a total of 5 set of orders camenum_tasks_list [50, 60, 70, 60, 50];%Orders Come in an intervals of 5000 pose stepstime_steps [1,5000,10000,15000,20000,25000];%Initializing a poses celllocations_dash cell(size(bot_locs,1),1);for i 1:1:size(bot_locs,1)locations_dash{i} zeros(0,3);end​​for OrderSet 1:1:5fprintf(OrderSet %d has arrived, Two layer Decentralized Task Allotment has Began.\n, OrderSet);num_tasks num_tasks_list(OrderSet);[locations,final_battery,map, Tasks] DecentralizedTwoLayerTaskAllocaton(bot_locs,num_tasks,final_task_locs,battery, Tasks);for i 1:1:size(locations,1)final_task_locs(i, :) [locations{i}(size(locations{i},1),1), locations{i}(size(locations{i},1),2)];end%final_task_locs round(final_task_locs);final_task_locs double(ceil(final_task_locs));final_task_locs checkOcc(final_task_locs);for i 1:1:size(locations,1)if ~isequal(locations_dash{i},locations{i})locations_dash{i} [locations_dash{i}; locations{i}];elselocations_dash{i} locations{i};endendbattery final_battery;m FindMaxTimeStep(locations_dash);if m time_steps(6)time_steps(6) m;endTstart time_steps(OrderSet);Tend time_steps(OrderSet1);fprintf(Simulation Has Started.\n);[bot_locs] Visualization(map,locations_dash,Tstart,Tend);end​​fprintf(Simulation of Task Allocation of 5 set of Orders has been Completed.\n);​​%Distance calculation of all robotsRobot_distances dCal(locations_dash, bot_locs);%Charging Visits by each robotCharge_visits Tasks(:,2);​%Plotting results%Distance travelled by each robotrobot_numbers 1:25;figure;bar(robot_numbers, Robot_distances);xlabel(Robot Number);ylabel(Distance Traveled);title(Distance Traveled by Each Robot(Decentralized with Battery));grid on;xlim([0.5, 25.5]);ylim([0 15000]);xticks(1:25);​%number of times charge station visited by each robotfigure;bar(robot_numbers, Tasks(:,2));xlabel(Robot Number);ylabel(Number of times went for charging);title(Number of times charge station visited by Each Robot(Decentralized with Battery));grid on;xlim([0.5, 25.5]);ylim([0 10]);xticks(1:25);​​%%%%%Functions Used%%%%%%%function [locations,final_battery,map, Tasks] DecentralizedTwoLayerTaskAllocaton(bot_locs,num_tasks,final_task_locs,battery,Tasks)%%%%%%%%%%%%%%%%%%%%%%%%%%%clustering based on proximitymin_diff_between_centroids 10;C 0;for k2:1:10if (C ~ 0)%calculating min distance between any two pointsD pdist(C);D squareform(D);D D max(D(:))*eye(size(D)); % ignore zero distances on diagonals[minD,temp] min(D(:));min_dist_btw_centroids minD;if (min_dist_btw_centroids min_diff_between_centroids)break;endend[idx, C] kmeans(bot_locs, k);num_of_clusters k;end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%STEP 2%code to divide pod_locs into different zones based on zone centroids%get the task-zone priority matrix which is NXM, M being the number of%zones%Initializing pod locations according to warehouse plan.pod_locs zeros(768,3);%PX3 matrixx_stacks 16;%number of stacks of pods in the x directiony_stacks 4;%number of stacks of pods in the y directionz 1;%used for filling pod locationswhile(z1)for k 0:y_stacks-1for i 0:x_stacks-1for j 0:5pod_locs(z,:) [z,1105*i, 15 2*j 13*k];pod_locs(z1,:) [z1,1105*i2, 15 2*j 13*k];z z2;endendendend%Assume num_tasks pods are selected after order batch to robot task conversion.%Those pods are selected randomly for now.num_pods size(pod_locs, 1);random_indices randperm(num_pods, num_tasks);task_pods pod_locs(random_indices, :);%These are the selected pods%This counts list gives the number of robots in each zone.unique_values unique(idx);counts zeros(size(unique_values));for i 1:numel(unique_values) 参考文献[1]潘成浩.仓储物流机器人拣选路径规划仿真研究[D].中北大学[2026-07-07].DOI:CNKI:CDMD:2.1017.167306.往期回顾扫扫下方二维码