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🔥 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

1. 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。

2. 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

1. 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。

2. 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

1. 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。

2. 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。

3. 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);​b=1;b_dash = 0;while(b<=20 & b_dash<=9)bot_locs(b,:) = [7+8*b_dash, 2];bot_locs(b+1,:) = [7+8*b_dash, 4];b = b+2;b_dash = b_dash +1;endb_dash = 0;while(b<=25 & b_dash<=4)bot_locs(b,:) = [27+5*b_dash, 6];b = b+1;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(OrderSet+1);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 k=2:1:10if (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(z<=1)for k = 0:y_stacks-1for i = 0:x_stacks-1for j = 0:5pod_locs(z,:) = [z,1+10+5*i, 15 + 2*j + 13*k];pod_locs(z+1,:) = [z+1,1+10+5*i+2, 15 + 2*j + 13*k];z = z+2;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.

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