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Swarm Robotics, From Biology to Robotics Part 4

Tham khảo tài liệu 'swarm robotics, from biology to robotics part 4', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Bio-inspired search strategies for robot swarms 23 Avg peaks found Std deviation Found rate Success rate w out cluster reduction 3.7360 1.4375 41.5 29.2 w cluster reduction 3.3820 1.4888 37.6 21.8 Table 10. Final results showing average number of peaks found out of 9 peaks found rate and success rate for 500 iterations for the Rastrigin 2D function The results from the 2D Rastrigin function are not as good as the results from the 1D functions. The lower found rate is due primarily to the fact that the Rastrigin function is a hard function - the peaks do not stand out as prominently as the F3 or even the F4 peaks. In addition the 2D search space is much larger for the Rastrigin function we used a scale of -5.1 to 5.12 for both x and y while the 1D functions are only defined between 0 x 1. We increased the tolerance for the 2D results to 0.4 and it appeared that many cluster centroids were close to the actual peaks but unfortunately not within the tolerance radius. 6. Conclusions We developed and tested two biologically inspired search strategies for robot swarms. The first search technique which we call the physically embedded Particle Swarm Optimization pePSO algorithm is based on bird flocking and the PSO. The pePSO is able to find single peaks even in a complex search space such as the Rastrigin function and the Rosenbrock function. We were also the first research team to show that the pePSO could be implemented in an actual suite of robots. Our experiments with the pePSO led to the development of a robot swarm search strategy that did not require each bot to know its physical location. We based the second search strategy on the biological principle of trophallaxis and called the algorithm Trophallactic Cluster Algorithm TCA . We have simulated the TCA and gotten good results with multipeak 1D functions but only fair results with multi-peak 2D functions. The next step to improve TCA performance is to evaluate the clustering algorithm. It appears that many times .