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We propose a new sampling-based path planning algorithm, the Optimal Minimum Risk Rapidly Exploring Random Tree (MR-RRT*), that plans minimum risk paths in accordance with primary and secondary cost criteria. The primary cost criterion is a user-defined measure of accumulated risk, which may represent proximity to obstacles, exposure to threats, or similar. Risk is only penalized in areas of the configuration...
This paper considers the problem of planning a path for an autonomous vehicle from a start to a goal location in presence of sensor intermittency modeled as a stochastic process, in addition to process and measurement noise. The aim is to plan a path that minimizes the localizational uncertainty for the vehicle upon arriving at the goal location. The main contribution of this paper is two-fold. We...
Conventional methods for rotation angle estimation are not very robust to variations in object shape or intensity. However in real object recognition scenarios like in underwater sonar images, the object seldom retains the same appearance in different test cases. Object representation using Zernike moments allows to capture these variabilities in a way that makes it robust in the context of rotation...
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