Efficient Large Scale Multi-Drone Delivery Using Transit Networks [ArXiv] [Github]
Shushman Choudhury, Kiril Solovry, Mykel J. Kochenderfer, and Marco Pavone
Submitted to IEEE ICRA 2020
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning [ArXiv] [Github]
Shushman Choudhury, Jacob P. Knickerbocker, and Mykel J. Kochenderfer
IEEE Intelligent Vehicles Symposium 2019
Incorporating Qualitative Information into Quantitative Estimation via Sequentially Constrained
Hamiltonian Monte Carlo Sampling [PDF]
Daqing Yi, Shushman Choudhury, and Siddhartha Srinivasa
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017
Densification Strategies for Anytime Motion Planning over Large Dense Roadmaps [ArXiv]
Shushman Choudhury, Oren Salzman, Sanjiban Choudhury, and Siddhartha Srinivasa
IEEE International Conference on Robotics and Automation (ICRA) 2017
Pareto-Optimal Search over Configuration Space Beliefs for Anytime Motion Planning [PDF]
Shushman Choudhury, Christopher Dellin, and Siddhartha Srinivasa
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016
A System for Multi-Step Mobile Manipulation: Architecture, Algorithms, and Experiments
Siddhartha Srinivasa et al.
International Symposium on Experimental Robotics 2016
Currency Recognition on Mobile Phones [PDF]
Suriya Singh, Shushman Choudhury, Kumar Vishal, and C. V. Jawahar
International Conference on Pattern Recognition (ICPR). IEEE, 2014
Anytime Geometric Motion Planning on Large Dense Roadmaps [PDF] [Github]
MS Thesis, Robotics Institute, Carnegie Mellon University, July 2017
During my PhD thus far, I have been researching decision-making under uncertainty for dynamic multimodal settings
Such problem solving typically requires choices in both discrete and continuous spaces
and robustness to uncertainty. I am currently working on real-time routing
for autonomous vehicles and dynamic assignment and scheduling for multi-robot systems.
My MS thesis proposed an algorithmic framework for efficient anytime geometric motion planning
on large and dense roadmaps
. We explored two key ideas in this space.
First, we framed the problem of anytime planning on roadmaps as one of searching for the shortest
path over a sequence of subgraphs of the entire roadmap graph. We studied the space of subgraphs and
formulated densification strategies to traverse the space of subgraphs.
Second, we developed an anytime roadmap planning algorithm,
that is efficient with respect to expensive collision checks, for searching each subgraph generated by the densification strategy.
This algorithm searches for paths that are Pareto-optimal in path length and collision probability
(obtained from some belief model) and adjusts the tradeoff to find successively shorter feasible paths.
We analyzed and implemented our framework and showed favourable performance with respect to current
approaches to anytime motion planning. Please see my papers at IROS 2016 and ICRA 2017
for the individual ideas and my thesis for the full framework.