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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
In today’s era of automation and intelligence, the application range of robots is becoming more and more extensive. Robots are capable of performing a variety of tasks, from industrial production to service industries, from healthcare to agriculture. However, as tasks increase and their complexity increases, how to efficiently allocate tasks and plan robot paths has become a key issue. In this article, we will introduce a robot multi-task allocation and path planning algorithm process based on the Dubins algorithm.
Dubins’ algorithm is an algorithm for planning robot paths. It finds the best path by considering the robot’s motion constraints and environmental constraints. The core idea of the Dubins algorithm is to model the robot’s motion model as a Dubins curve, which is a special type of curve consisting of straight line segments and arc segments. The Dubins algorithm finds a shortest path by searching the Dubins curve between the robot’s initial position and the target position.
In multi-task allocation and path planning problems, we need to consider the relationship between multiple tasks and multiple robots. First, we need to determine the priority and constraints of each task. We then need to assign tasks to the available robots and plan each robot’s path. Finally, we need to consider collaboration and conflict resolution between robots.
The algorithm flow is as follows:
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Enter the task list and robot list.
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Assign unique identifiers to each task and robot.
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Sort tasks from high to low according to their priority and constraints.
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Go through the task list and choose the best robot for each task. Greedy algorithms or other heuristics can be used for task allocation.
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For each task, the Dubins algorithm is used to plan the robot’s path. First, determine the starting position and target position of the robot. Then, use Dubins algorithm to find the best path.
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If there are multiple robots performing tasks simultaneously, collaboration and conflict resolution between robots need to be considered. Coordination algorithms can be used to resolve conflicts between robots.
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Output task allocation and path planning results.
By using the robot multi-task allocation and path planning algorithm process based on the Dubins algorithm, we can efficiently allocate tasks and plan the robot’s path. This algorithm can be applied in various fields such as industrial automation, logistics and traffic management. It can improve the efficiency and accuracy of task execution while reducing conflicts and collisions between robots.
To sum up, robot multi-task allocation and path planning is a complex and critical issue. By using Dubins algorithm, we can find the best path and allocate tasks efficiently. This algorithm can be applied to various practical scenarios and contribute to the development of automation and intelligence fields. We hope that the introduction of this article can be helpful to the research and practice of robot multi-task allocation and path planning algorithms.
Part of the code
function [xa,ya,xb,yb] = dubinsCalculateFunction(R,x0,y0,phi1,a0,b0,theta1)</code><code>?</code><code>phi0=phi1/pi *180;</code><code>x=x0 + R*cos((phi0 + 90)/57.3); %Counterclockwise circle center - starting circle</code><code>y=y0 + R* sin((phi0 + 90)/57.3);</code><code>?</code><code>x1=x0 + R*cos((phi0-90)/57.3); %Clockwise circle center-- Starting circle</code><code>y1=y0 + R*sin((phi0-90)/57.3);</code><code>?</code><code>theta0=theta1/pi*180; </code><code>a=a0 + R*cos((theta0 + 90)/57.3); %Counterclockwise circle center - terminal circle</code><code>b=b0 + R*sin((theta0 + 90)/57.3);</code><code>?</code><code>a1=a0 + R*cos((theta0-90)/57.3); %Clockwise circle center--terminal circle</code> code><code>b1=b0 + R*sin((theta0-90)/57.3);</code><code>?</code><code>?</code><code>% circle center distance</code> code><code>L1=sqrt((x-a)*(x-a) + (y-b)*(y-b));</code><code>L2=sqrt((x-a1)*(x-a1) + ( y-b1)*(y-b1));</code><code>L3=sqrt((x1-a)*(x1-a) + (y1-b)*(y1-b));</code code><code>L4=sqrt((x1-a1)*(x1-a1) + (y1-b1)*(y1-b1));</code><code>?</code><code>[ Lmin,index] = min([L1 L2 L3 L4]); </code><code>switch index</code><code> case 1%x,y a,b inverse of starting circle and inverse of ending circle</code> <code> </code><code> %left to left</code><code>% alpa=pi/2 + atan(R/L1);</code><code> alpa=pi/2;</code> code><code> F=[a;b] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[x-a;y-b]*R/L1;</code><code> xb=F(1); %tangent point coordinates</code><code> yb=F(2);</code><code> alpa=alpa + pi;</code><code> I=[x ;y] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[a-x;b-y]*R/L1;</code><code> xa=I(1); % Starting point coordinates</code><code> ya=I(2);</code><code> </code><code> case 2%x,y a1,b1 start circle inverse end circle forward</code> <code> </code><code> %left to right</code><code>% alpa=-pi/2 + atan(2*R/L2);</code><code> alpa=-(pi /2-asin(2*R/L2));</code><code> F=[a1;b1] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[ x-a1;y-b1]*R/L2;</code><code> xb=F(1); %tangent point coordinates</code><code> yb=F(2);</code><code> I=[x;y] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[a1-x;b1-y]*R/L2;</code><code> xa=I(1); % starting point coordinates</code><code> ya=I(2);</code><code> </code><code> case 3%x1,y1 a,b onwards The starting circle is inverse and the ending circle is straight</code><code> </code><code> %right to left</code><code>% alpa=pi/2-atan(2*R/L1);</code><code> alpa=pi/2-asin(2*R/L3);</code><code> F=[a;b] + [cos(alpa) -sin(alpa);sin(alpa) cos (alpa)]*[x1-a;y1-b]*R/L3;</code><code> xb=F(1); %tangent point coordinates</code><code> yb=F(2) ;</code><code> I=[x1;y1] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[a-x1;b-y1]*R/L3 ;</code><code> xa=I(1); % starting point coordinates</code><code> ya=I(2);</code><code> </code><code> case 4%x1 ,y1 a1,b1 starting rounding and ending rounding</code><code> </code><code> %right to right</code><code>% alpa=-pi/2 + atan(R/L4 );</code><code> alpa=-pi/2;</code><code> F=[a1;b1] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa) ]*[x1-a1;y1-b1]*R/L4;</code><code> xb=F(1); %Tangential point coordinates</code><code> yb=F(2);</code><code> alpa=alpa + pi;</code><code> I=[x1;y1] + [cos(alpa) -sin(alpa);sin(alpa) cos(alpa)]*[a1- x1;b1-y1]*R/L4;</code><code> xa=I(1); % starting point coordinates</code><code> ya=I(2);</code><code> </code><code> otherwise</code><code> error('exception')</code><code>end</code><code>?</code><code>end</code><code>?</code><code>?
Operation results
References
[1] Zhang Lanyong, Han Yu. AUV cluster path planning based on improved RRT* algorithm [J]. Chinese Ship Research, 2023, 18(1):9.DOI:10.19693/j.issn.1673-3185.02879.
[2] Qi Jian. Research on multi-robot path planning based on music wall [D]. Anhui University of Science and Technology, 2017. DOI: 10.7666/d.Y3236505.
[3] Tang Bojian. Research on task allocation and path planning of multi-mobile sandblasting robots [D]. Tianjin University of Science and Technology [2023-11-03].
[4] Shan Fang. Research on robot path planning based on improved ant colony algorithm[D]. Tianjin University of Finance and Economics[2023-11-03].DOI:CNKI:CDMD:2.2006.071966.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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