The novel Generalized Shape Expansion (GSE) algorithm and its variants help in sampling-based exploration of an environment and generating feasible near-optimal path.

Development of probabilistic higher-order velocity obstacles algorithm for collision avoidance in an unknown obstacle-cluttered environment. The right-of-way is embedded to make it more suitable for practical systems.

Various algorithms (Maxima Turn Switching, Gradient Direction Turn Switching) are developed to locate the source by exploring the environment and moving toward the source simultaneously.

The L1 path following guidance has gained popularity recently due to its simplicity.  To improve the performance under different radii of curvature, the variable L1 path-following guidance strategy is developed.

SMC-based guidance laws for  autonomous landing of UAVs on stationary/moving/maneuvering ground vehicles at desired approach angles are developed.

By improvising the classical PN guidance, additional objectives (approaction angle and time) are achieved.  The navigation gain is adjusted to approach the destination at the desired time and orientation. 

SMC-based instantaneously optimal guidance law for precision landing of spacecraft under the variable gravitational field is developed.  

Variable gain gradient descent for parameter update law of critic for RL-based realization of optimal control of UAV movement under bounded uncertainties.