Shreyas Raorane

Shreyas Raorane, Kabir Ram Puri, Anh-Quan Pham

Autonomous robots operating in dynamic environments must balance global path optimality with real-time responsiveness to disturbances. This requires addressing a fundamental trade-off between computationally expensive global planning and fast local adaptation. Sampling-based planners such as RRT* produce near-optimal paths but struggle under perturbations, while dynamical systems approaches like SEDS enable smooth reactive behavior but rely on offline data-driven optimization. We introduce Sampling-Based Adaptive Motion Planning (SBAMP), a hybrid framework that combines RRT*-based global planning with an online, Lyapunov-stable SEDS-inspired controller that requires no pre-trained data. By integrating lightweight constrained optimization into the control loop, SBAMP enables stable, real-time adaptation while preserving global path structure. Experiments in simulation and on RoboRacer hardware demonstrate robust recovery from disturbances, reliable obstacle handling, and consistent performance under dynamic conditions.

Kedar Karpe, Avinash Sinha, Shreyas Raorane et al.

This paper presents a distributed multi-robot printing method which utilizes an optimization approach to decompose and allocate a printing task to a group of mobile robots. The motivation for this problem is to minimize the printing time of the robots by using an appropriate task decomposition algorithm. We present one such algorithm which decomposes an image into rasterized geodesic cells before allocating them to the robots for printing. In addition to this, we also present the design of a numerically controlled holonomic robot capable of spraying ink on smooth surfaces. Further, we use this robot to experimentally verify the results of this paper.