A robotic system continuously measures its own motions and the external world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate system. A nontrivial robotic system has a large number of different frames and data have to be translated back-and-forth from a frame to another. The onus is on the developers to get such translation right. However, this is very challenging and error-prone, evidenced by the large number of questions and issues related to frame uses on developers' forum. Since any state variable can be associated with some frame, reference frames can be naturally modeled as variable types. We hence develop a novel type system that can automatically infer variables' frame types and in turn detect any type inconsistencies and violations of frame conventions. The evaluation on a set of 180 publicly available ROS projects shows that our system can detect 190 inconsistencies with 154 true positives. We reported 52 to developers and received 18 responses so far, with 15 fixed/acknowledged. Our technique also finds 45 violations of common practices.

Wide-field integration (WFI) methods, which are based on the spatial decompositions of optic flow performed by motion sensitive tangential neurons within the insect visuomotor system, have been shown to be an efficient and robust way to extract visual cues for guidance and navigation. In this paper, previous analytical results on WFI-based autonomous local guidance are validated on a wheeled robotic platform. Successful corridor navigation is achieved through closed loop feedback of the first two spatial harmonics of the optic flow, which provide the lateral and rotational stiffness with respect to local obstacles required to stabilize a collision-free trajectory through the environment.