Research InterestsMy current research focuses on developing theoretical foundations and designing efficient algorithms for coordination and motion planning of robots. My Ph.D. thesis unifies coverage of point, curve, and area features in environments into a novel generalized coverage framework, formalized as optimization problems on graphs. We used the formalization to design approximation algorithms with provable guarantees and heuristic algorithms for fast large-scale applications, validating them extensively in simulations and experiments. Prior to the Ph.D., my research comprised analyzing and designing mechanisms and parallel robots using optimization techniques. My experience includes using symbolic algebra systems, developing open-source libraries, leading research projects, and mentoring students.
Building on the current work, my long-term research goal is to develop autonomous systems for gathering reliable information about the environment. I strive to develop theory and scalable algorithms while incorporating artificial intelligence for energy-efficient and risk-aware robot motion planning and swarming.
Coverage of Linear Features using Multiple Robots
Wind considerations in computing the energy consumed while traversing
The battery life is modeled as a constraint on the length of the tours
Optimize the tours for the total travel cost of all the robots
The amount of data gathered is significantly lesser than current solutions, thereby reducing the computation required to analyze the environment
Applications include inspection of road networks and power lines
Development of algorithms to simultaneously compute the optimal assignments and formation parameters for a team of robots from a given initial formation to a variable goal formation.
The shape of goal formation is provided as input
The scale and location parameters for the goal formation is optimized
Optimal assignments of the robots to the goal positions
Sum of squared travel distance is minimized
Guaranteed collision-free trajectories
Robots start simultaneously and reach their goal positions simultaneously
O(n^3) running time complexity