Researchers at the Indian Institute of Technology (IIT), Madras have developed a class of fast and efficient “motion planning” algorithms which can think like human beings and enable autonomous aerial, ground or surface vehicles to navigate obstacle-cluttered environments. According to the team, the algorithms have been developed on a novel notion of ‘Generalised Shape Expansion’ (GSE) that enables planning for a safe and dynamically feasible trajectory for autonomous vehicles.
These approaches have been found to yield superior results compared to many of the existing seminal and state-of-the-art motion planning algorithms. Because of its novel calculation of “safe” region, it provides a crucial advance during time-sensitive planning scenarios arising in applications like self-driving cars, disaster response, ISR operations, aerial drone delivery and planetary exploration, among others, the team claimed.
Unmanned Aerial Vehicles (UAVs) are often deployed to survey affected regions and scan debris for search and rescue missions. Since in such applications, UAV paths need to be planned in advance in a time-critical manner, these algorithms can play a key role, they said. The research led by Satadal Ghosh, Assistant Professor, Department of Aerospace Engineering, IIT Madras, has published several research papers in internationally reputed peer-reviewed journals like AIAA Journal of Guidance, Control, and Dynamics, and IEEE Control Systems Letters, and top-tier conferences like IEEE Conference on Decision and Control (CDC), American Control Conference (ACC) and AIAA SciTech.
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