Tham khảo tài liệu 'swarm robotics, from biology to robotics part 3', 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 13 this paper the wait time was exponentially related to the measurement value we experimented with linear wait times as well. We did step 4 determine the clusters when the search is done . In general the bots begin to collide stop wait at the beginning. Thus the bots tend to cluster soon after the search begins so the search can be stopped at any time to observe the location s of the clusters. In 2D the clusters tend to become more pronounced as the iterations increase so waiting a longer time can make the position s of the peak s more obvious. Related work The TCA is based on the work of Thomas Schmickl and Karl Crailsheim Schmickl Crailsheim 2006 Schmickl Crailsheim 2008 who developed the concept based on the trophallactic behavior of honey bees. Schmickl and Crailsheim use the trophallactic concept to have a swarm of bots move simulated dirt from a source point to a dump point. The bots can upload nectar from the source point where the amount of nectar for each bot is stored in an internal variable. As the robots move the amount of stored nectar decreases so the higher the nectar level then the closer to the source. Each robot also queries the nectar level of the robots in the local neighborhood and can use this information to navigate uphill in the gradient. There is a also a dump area where the loaded robots aggregate and drop the dirt particles. The swarm had to navigate between the source and the dump and achieved this by establishing two distinct gradients in parallel. Their preliminary results showed a problem where the bots tended to aggregate near the dump and the source. When that happened the bridge or gradient information between the source and the dump was lost. To prevent the aggregation they prevented a percentage of their robots from moving uphill and just performed a random walk with obstacle avoidance. Even though the work of Schmickl and Crailsheim is significant they show no published .
