IIT-M develops base for self-driving cars
New Delhi, Dec. 19: Researchers at 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-ofthe-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.
The research led by Satadal Ghosh, assistant professor, Department of Aerospace Engineering, IIT Madras and the team included IIT Madras alumni Vrushabh Zinage, Adhvaith Ramkumar, and Nikhil P.
"The GSE-based algorithms function by calculating a 'safe' region consisting of large 'visible' areas in the environment, customised to ensure navigability. Following this, the algorithms select a random point in this 'visible' region and connect it through a safe 'edge' to the safely reachable regions discovered so far. Eventually, the algorithms can almost always connect any two points in any environment, which satisfies certain basic criteria," Zinage said.
"Broadly speaking, the class of GSE-based algorithms has promising potential in autonomous applications ranging over warehouse material movement, inspection of project commissioning, drone delivery, disaster management, self-driving cars, and so on," Ghosh said.
The current status of this research, as per the team, is limited to theoretical development and improvement of the GSE-based algorithms and extensive realistic simulation-based validation of the same.