EIRIS - An Extended Proposition Using Modified Occupancy Grid Map and Proper Seeding

摘要

With a view of robotic path planning inside any cluttered and confined indoor environment, a-priori generation of convex free-spaces offers additional benefit to the motion planner. Traditionally the convex free-space grows from a seed, which either selected randomly or generated from a heuristic function. However arbitrary seeding cannot guarantee maximal free-space coverage. In addition, improper seeding may ignore a valuable free-space information (e.g., narrow corridor, broken window, partially closed door etc.), because of which planning might fail. In the current work, a logically Extended Iterative Regional Inflation by Semidefinite Programming (EIRIS) is proposed to address the problem. A modified occupancy grip map (MOGM) creates an approximate binary map of the environment. It is thereby used to identify the mutually isolated largest possible free-regions. For each free-region proper seeding (PS) algorithm allows the generated seed to inflate along a coordinate axis. Unlike IRIS where unguided growth results in random free-space generation, the greedy expansion of a seed is inhibited temporarily by treating all the free-regions as virtual obstacles other than the region of interest. It is a guided iterative inflation (GIl) algorithm. Regarding robotic path planning the convex free-space information needs to be contiguous and subsequently traditional graph search technique can be exploited for efficient path generation. But it is beyond the scope of present discussion and considered as a future work. However few typical paths are shown in the paper to emphasize the utility of free-space generation in prior of path planning. Simulation results with empirical analysis establish the improvement of proposed method (EIRIS) over the state-of-the-art approach.