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The paper presents a general object representation for efficient collision detection. Spatial representation is a crucial factor when motion planning is applied to real environments with complex objects, since the efficiency of the collision detection algorithms depends on the spatial representation used for the agents and obstacles in the scene. Our spatial model consists of a hierarchy of representations that approximates the object at different levels of detail. The input to the system is any object that could be described using non-homogeneous generalized cylinders. An algorithm that automatically converts between polyhedral approximations and generalized cylinders extends the applicability of the approach. Compared with related approaches in the literature, our system can deal with concave and curved objects, it is spatially balanced, complete, stable and converges to a zero-error model. Experimental results show that better approximations are obtained (in terms of quality), and computation times are even two orders of magnitude less. The approach has been applied to solve collisions in a scene where human beings and robots interact. This is a fundamental requirement towards human-safe service robotics.
Martínez‐Salvador et al. (Thu,) studied this question.