【路径规划】分布式目标检测和跟踪的多无人机matlab源码

不同的技术和环境条件下，协调多个无人机以进行分布式目标检测和跟踪。提议的方法基于多代理系统中的群体行为的概念，即由无人机组成的自我组建和自我协调的团队，可使其适应特定任务的环境布局。蜂群的形成和协调分别受植绒和双能生物机理的启发。通过适当组合这些机制，可以在环境中的全局搜索（探索）和局部搜索（开发）之间取得适当的平衡。群体适应基于进化算法，其目的是在任务期间最大化跟踪目标的数量或最小化目标发现的时间。

% set title
optimInfo.title = 'Demo 1 (Rosenbrock''s saddle)';

% specify objective function
objFctHandle = @rosenbrocksaddle;

% define parameter names, ranges and quantization:

% 1. column: parameter names
% 2. column: parameter ranges
% 3. column: parameter quantizations
% 4. column: initial values (optional)


paramDefCell = {
	'parameter1', [-3 3], 0.01
	'parameter2', [-3 3], 0.01
};

% set initial parameter values in struct objFctParams 
objFctParams.parameter1 =  -2;
objFctParams.parameter2 = 2.5;

% set single additional function parameter
objFctSettings = 100;

% get default DE parameters
DEParams = getdefaultparams;

% set number of population members (often 10*D is suggested) 
DEParams.NP = 20;

% do not use slave process here
DEParams.feedSlaveProc = 0;

% set times
DEParams.maxiter  = 20;
DEParams.maxtime  = 30; % in seconds
DEParams.maxclock = [];

% set display options
DEParams.infoIterations = 1;
DEParams.infoPeriod     = 10; % in seconds

% do not send E-mails
emailParams = [];

% set random state in order to always use the same population members here
setrandomseed(1);

% start differential evolution
[bestmem, bestval, bestFctParams, nrOfIterations, resultFileName] = differentialevolution(...
	DEParams, paramDefCell, objFctHandle, objFctSettings, objFctParams, emailParams, optimInfo); %#ok

disp(' ');
disp('Best parameter set returned by function differentialevolution:');
disp(bestFctParams);

% continue optimization by loading result file
if DEParams.saveHistory
  
  disp(' ');
  disp(textwrap2(sprintf(...
    'Now continuing optimization by loading result file %s.', resultFileName)));
  disp(' ');
  
  DEParams.maxiter = 100;
  DEParams.maxtime = 60; % in seconds

  [bestmem, bestval, bestFctParams] = differentialevolution(...
    DEParams, paramDefCell, objFctHandle, objFctSettings, objFctParams, emailParams, optimInfo, ...
    resultFileName); %#ok
  
  disp(' ');
  disp('Best parameter set returned by function differentialevolution:');
  disp(bestFctParams);
end

1. M. Avvenuti, M.G.C.A. Cimino, G. Cola, G. Vaglini, "Detection and mapping of a toxic cloud using UAVs and emergent techniques", in Proc. MIKE The Sixth International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2018), Vol -, pp. 1-10, Cluj-Napoca, Romania, 20-22 December, 2018.
2. A.L. Alfeo, M.G.C.A. Cimino, N. De Francesco, A. Lazzeri, M. Lega, G. Vaglini, "Swarm coordination of mini-UAVs for target search using imperfect sensors", Intelligent Decision Technologies, IOS Press, Vol. 12, Issue 2, Pages 149-162, 2018.
3. A.L. Alfeo, M.G.C.A. Cimino, N. De Francesco, M. Lega, G. Vaglini, "Design and simulation of the emergent behavior of small drones swarming for distributed target localization", Journal of Computational Science, Elsevier Science, Vol. 29, Pages 19-33, 2018.
4. M.G.C.A. Cimino, A. Lazzeri, G. Vaglini, "Using Differential Evolution to Improve Pheromone-based Coordination of Swarms of Drones for Collaborative Target Detection", in Proc. INSTICC The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pp. 605-610, Rome, Italy, 2016.
5. M.G.C.A. Cimino, A. Lazzeri, G. Vaglini "Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search", in Proc. IEEE The Sixth International Conference on Information, Intelligence, Systems and Applications (IISA 2015), pp. 1-6, Corfu, Greece, pp.1-6, 6-8 July 2015.

6. 完整代码或者代写添加QQ1575304183

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