INRC Forum: Akshit Saradagi

Neuromorphic sensing in sub-terranean environments and neuromorphic solvers for model predictive control

Abstract: In this talk, I will be presenting some recent results in Neuromorphic Engineering from the Robotics and AI group at Luleå University of Technology, Sweden.
In the first half of my talk, I will be presenting a novel LiDAR and event camera fusion framework for fast and precise object and human detection in subterranean (SubT) environments. The fusion framework caters to the wide variety of adverse lighting conditions found in SubT environments, such as low or no light, high-contrast zones and in the presence of blinding light sources. The proposed fusion uses intensity filtering and K-means clustering on the LiDAR point cloud and frequency filtering and connectivity clustering on the events induced in an event camera by the returning LiDAR beams. The fusion framework was experimentally validated in a real SubT environment (a mine) with a Pioneer 3AT mobile robot. The experimental results show real-time performance for human detection and the NMPC-based controller allows for reactive tracking of a human or object of interest, even in complete darkness.
In the second half of the talk, I will be presenting our preliminary results on using neuromorphic solvers for solving quadratic programs arising in Model Predictive Control (MPC). More specifically, we employed the floating-point LAVA QP solver, which emulates the Proportional-Integral Projected Gradient (PIPG) Method for solving QP problems, to solve terminally constrained MPC problems. The objective function in linear MPC problems being strongly convex, the LAVA QP solver ensures that the distance to optimum and the constraint violation converge to zero at the rate of O(1/k^2) and O(1/k^3) respectively, with ‘k’ being the number of solver iterates. Given this peculiar convergence property of the solver, I will present a sketch of our proof for asymptotic stability of the closed loop system, along with the simulation-based validation.

Bio: Akshit Saradagi is a Postdoctoral researcher in the Robotics and AI group at Luleå University of Technology, Sweden. He received his M.S and Ph.D dual degree from the Indian Institute of Technology Madras (IITM), Chennai, India. His current research focusses on distributed control of multi-agent systems, control barrier functions-based safety guarantees in Robotics, applications of Neuromorphic Computing in Robotics and control under resource constraints.

For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).