Patent

Publications

A look at motion planning for AVs at an intersection

Published in 21st International Conference on Intelligent Transportation Systems (ITSC), 2018

A look back at different motion planning approaches taken while navigating an intersection.

Recommended citation: S. Krishnan, R. Govind Aadithya, R. Ramakrishnan, V. Arvindh and K. Sivanathan, "A Look at Motion Planning for AVs at an Intersection," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 333-340, doi: 10.1109/ITSC.2018.8569244. https://ieeexplore.ieee.org/document/8569244

Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning

Published in 4th International Symposium On Robotics and Manufacturing Automation, DSEC, Perambalur, Tamil Nadu, India., 2018

A decentralized trajectory planning approach while optimizing for minizing jerk. The approach proposes a safe region formulation that takes into account the static and dynamic obstacles while trajectory optimization.

Recommended citation: Arvindh, Vijay, Govind Aadithya R, and Shravan Krishnan. "Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning." arXiv e-prints (2018): arXiv-1812. https://arxiv.org/pdf/1812.00868.pdf

Towards Scalable Continuous‑Time Trajectory Optimization for Multi‑Robot Navigation

Published in ArXiv, 2019

Building on the Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning work, we extended the framework to account for the interactions like the downwash of the robot, size of the robot, and explicit dynamic collision avoidance with soft constraints while solving for trajectory.

Recommended citation: Krishnan, S., Rajagopalan, G. A., Kandhasamy, S., & Shanmugavel, M. (2019). Towards Scalable Continuous-Time Trajectory Optimization for Multi-Robot Navigation. https://arxiv.org/pdf/1910.13463.pdf

Continuous-time trajectory optimization for decentralized multi-robot navigation

Published in Advances in Control and Optimization of Dynamic Systems 2020, IIT Madras, India, 2020

Continuing the work from Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning, in this paper we propose a trajectory optimization framework where we estimate the trajectories of the other robotis as a polynomial function of time hence accounting for tentative collision zones that should be avoided by the ego while planning the trajectory.

Recommended citation: Shravan Krishnan, Govind Aadithya Rajagopalan, Sivanathan Kandhasamy, Madhavan Shanmugavel, "Continuous-Time Trajectory Optimization for Decentralized Multi-Robot Navigation", IFAC-PapersOnLine,Volume 53, Issue 1,2020,Pages 494-499,ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2020.06.083

Thesis

Unpublished Works