3 citations · 6 across the 6 of their papers we have counts for
6 papers
A First-Order Numerical Algorithm without Matrix Operations
Muhammad Adil, Ramtin Madani, Sasan Tavakkol +1
This paper offers a matrix-free first-order numerical method to solve large-scale conic optimization problems. Solving systems of linear equations pose the most computationally cha…
Machine Learning Based Relative Orbit Transfer for Swarm Spacecraft Motion Planning
Alex Sabol, Kyongsik Yun, Muhammad Adil +2
In this paper we describe a machine learning based framework for spacecraft swarm trajectory planning. In particular, we focus on coordinating motions of multi-spacecraft in format…
Optimal Multi-Robot Motion Planning via Parabolic Relaxation
Changrak Choi, Muhammad Adil, Amir Rahmani +1
Multi-robot systems offer enhanced capability over their monolithic counterparts, but they come at a cost of increased complexity in coordination. To reduce complexity and to make…
Rapid Convergence of First-Order Numerical Algorithms via Adaptive Conditioning
Muhammad Adil, Sasan Tavakkol, Ramtin Madani
This paper is an attempt to remedy the problem of slow convergence for first-order numerical algorithms by proposing an adaptive conditioning heuristic. First, we propose a paralle…
Multi-Agent Motion Planning using Deep Learning for Space Applications
Kyongsik Yun, Changrak Choi, Ryan Alimo +5
State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the co…
Convex Relaxation of Bilinear Matrix Inequalities Part II: Applications to Optimal Control Synthesis
Mohsen Kheirandishfard, Fariba Zohrizadeh, Muhammad Adil +1
The first part of this paper proposed a family of penalized convex relaxations for solving optimization problems with bilinear matrix inequality (BMI) constraints. In this part, we…