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Advances in Robot Navigation Part 9

Tham khảo tài liệu 'advances in robot navigation part 9', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Navigation Among Humans 149 output values PI and input values pose and velocity were logged for later analysis during both experiments. In Experiment 1 the objective was to see if the system was capable of learning to estimate PI based on interaction experience from several different people. As the number of cases increase the system should be better able to estimate PI of a person and do it more quickly. Furthermore the information in the CBR database should be generic such that information obtained with some people can be used when other people occur. Starting from a CBR system with no knowledge i.e. an empty database a total of five test persons were asked to approach or pass the robot 12 times using different motion paths which are illustrated in Fig. 8. The starting and end points of each session were selected randomly while the specific route was chosen by the test person. The random selection was designed so the test persons would end up with close interaction in 50 of the sessions. In the other sessions the test persons would pass the robot either to the left of the right without interacting. In Experiment 2 the objective was to test the adaptiveness of the CBR system. The system should be able to change its estimation of PI over time if behaviour patterns change. A total of 36 test approaches were performed with one test person. The test person would start randomly at P1 P2 or P3 see Fig. 8 and end the trajectory at P4 P5 or P6. In the first 18 sessions the test person would indicate interest in close interaction by handing an object to the robot from P5 while in the last 18 sessions the person did not indicate interest and the trajectory ended at P4 or P6. 9.3 Simulations in crowded environments The algorithm for trajectory planning in crowded environments described in Section 7 is implemented and demonstrated to work on a simulated pedestrian street as shown in Fig. 5. The experiments consist of two parts. First the algorithm is applied on the .