Optimal motion planning and stopping test for 3-D object reconstruction

In this work, two aspects of motion planning for object reconstruction are investigated. First, the effect of using a sampling-based optimal motion planning technique to move a mobile manipulator robot with 8 degrees of freedom, during the reconstruction process, in terms of several performance criteria is studied. Based on those criteria, the results of the reconstruction task using rapidly exploring random tree (RRT) approaches are compared, more specifically RRT* smart versus RRT* versus standard RRT. Second, the problem of defining a convenient stopping probabilistic test to terminate the reconstruction process is addressed. Based on our results, it is concluded that the use of a RRT* improves the measured performance criteria compared with a standard RRT. The simulation experiments show that the proposed stopping test is adequate. It stops the reconstruction process when all the portions of object that are possible to be seen have been covered with the field of view of the sensor.