By Michael Mistry, Aleš Leonardis, Mark Witkowski, Chris Melhuish
This booklet constitutes the refereed lawsuits of the fifteenth convention on Advances in self sufficient Robotics, TAROS 2014, held in Birmingham, united kingdom, in September 2014. The 23 revised complete papers awarded including nine prolonged abstracts have been rigorously reviewed and chosen from forty eight submissions. the final application covers a variety of elements of robotics, together with navigation, making plans, sensing and notion, flying and swarm robots, ethics, humanoid robotics, human-robot interplay, and social robotics.
Read Online or Download Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings PDF
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Extra info for Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings
Figure 7 shows a simulation experiment performed using 1 robot and 1 obstacle: these images were generated by Webots except for the blue arrows included to show the robot orientation in each time step illustrated. The robot's initial behavior was as expected. However, a problem always happened when the robot An Improved Cellular Automata-Based Model for Robot Path-Planning 31 faced an obstacle and tried to overcome it by applying the CA deviation rules. The robot got stuck by the combination of rules and imprecise sensor readings, thus rotating in the same axis and not moving forward.
If there is an obstacle, a deviation rule will be applied. Otherwise, a formation control rule will be applied. Fig. 2. Spatial neighborhood There are two types of deviation rules: (i) those that makes the robot to move 1 cell; (ii) those that rotates the robot. The rules able to move the robot must be applied in pairs: the first rule is executed for the neighborhood for which the central cell is in the state Robot, indicating that the robot is within this cell (the next state will be Free) and the second one is executed for the neighborhood for which the central cell is in the state Free (this state will be changed to Robot).
Comparison of position estimates obtained from dead-reckoning (DR) and MCL, with corresponding standard deviation of the MCL’s particle set 22 M. Nitsche et al. (a) Training (b) MCL (c) Dead-reckoning (d) Training (e) MCL (f) Dead-reckoning Fig. 2. Drone was placed a few meters before the initial mapping point, which is reﬂected by MCL, but not by the dead-reckoning, see Figure 1(d). Drone’s ‘replay 1’ (see Figure 1(c)) it can be seen that after the initial convergence of MCL, the position estimates diﬀer only slightly.