Multi agent Path Planning for Optimal coverage of Agricultural farms
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Documents regarding my thesis.
Blog posts
Heuristically guided Sampling based Path Planning
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Sampling based Path planning algorithms suffer for optimality and convergence issues. Algorithms like RRT, Informed RRT etc. are said to asymptotically optimal; i.e. the algorithm converges to optimal solution as the number of samples tend to infinity. But there is no proper gaurentee given about the rate of convergence. To improve the rate of convergence, algorithms like BIT* perform heuristically guided search to converge faster towards optimalitily.
Autonomous Drone Navigation
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During the pandemic, I was at home without any access to any physical robots. It was the perfect time to put into practise the concepts of Robotics that I learnt in the summer. I got quite intriguied by Autonomous drones. My greatest inspirations came after watching this video on Deep Drone Racing by the Robotics and Perception group at UZH and a bunch of really cool work by the Autonomous Robots Lab at NTNU. Taking inspiration, I wanted to try out working with Indoor Navigation of Autonomous Drones.
Path Planning in Autonomous Robots
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Path planning is one of the most important primitives for autonomous mobile robots. The ability to be able to travel on its own by finding a collision free, optimal path is an important aspect of making robots autonomous. Path, as the name suggests is a set of waypoints which a Robot is expected to travel. There can be many criterions for deciding a path that the Robot should follow. Various optimisations, checks are made before deciding an optimial path.