/Search-based Planning Laboratory

Search-based Planning Laboratory

Portrait of Search-based Planning Laboratory
Lab Head: Maxim Likhachev
Lab Homepage
Mailing Address

Search-based Planning Laboratory researches methodologies and algorithms that enable autonomous systems to act fast, intelligently and robustly. Our research concentrates mostly on developing novel planning approaches, coming up with novel heuristic searches and investigating how planning can be combined with machine learning. Our work spans graph theory, algorithms, data structures, machine learning and of course robotics. We use our algorithms to build real-time planners for complex robotic systems operating in real world and performing challenging tasks ranging from autonomous navigation and autonomous flight to multi-agent systems and to full-body mobile manipulation.

In a bit more details, we study such problems as high-dimensional motion planning, task planning, planning under uncertainty and multi-agent planning. Our goal is to develop planners that work in real-time and deal with complex real-world environments. We are also actively pursuing planning approaches that “learn from experience”. In all of our work, we strive to develop methods that come with rigorous analytical guarantees on performance such as completeness and bounds on sub-optimality. Such guarantees help dramatically users to analyze and anticipate the behavior of autonomous systems which is crucial for safe autonomy alongside people. The lab is home to several robots including PR2 robot, segbot robot, hexarotor aerial vehicle, quadrotor aerial vehicles and few other smaller aerial vehicles. In addition, we build planners for a number of large-scale robotic systems such as humanoid robots and full-scale helicopter.

Displaying 12 Publications
Autonomous Flight and Navigation in Air-Ground Systems
Benjamin Holden

Master's Thesis, Tech. Report, CMU-RI-TR-16-42, Robotics Institute, Carnegie Mellon University, August, 2016
A*-Connect: Bounded Suboptimal Bidirectional Heuristic Search
Fahad Islam, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2016
PERCH: Perception via Search for Multi-Object Recognition and Localization
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2016
Efficient Search with an Ensemble of Heuristics
Michael Phillips, Venkatraman Narayanan, Sandip Aine and Maxim Likhachev

Conference Paper, International Joint Conference on Artificial Intelligence (IJCAI), July, 2015
Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics
Venkatraman Narayanan, Sandip Aine and Maxim Likhachev

Conference Paper, International Symposium on Combinatorial Search (SoCS), June, 2015
Dynamic Multi-Heuristic A*
Fahad Islam, Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2015
Task-Oriented Planning for Manipulating Articulated Mechanisms Under Model Uncertainty
Venkatraman Narayanan and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2015
Multi-Heuristic A*
Sandip Aine, Siddharth Swaminathan, Venkatraman Narayanan, Victor Hwang and Maxim Likhachev

Conference Paper, Robotics: Science and Systems, July, 2014
Motion Planning for Robotic Manipulators with Independent Wrist Joints
Kalin V. Gochev, Venkatraman Narayanan, Benjamin Cohen, Alla Safonova and Maxim Likhachev

Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2014
Planning Under Topological Constraints Using Beam Graphs
Venkatraman Narayanan, Paul Vernaza, Maxim Likhachev and Steven M. LaValle

Conference Paper, 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 431-437, May, 2013
Anytime Safe Interval Path Planning for Dynamic Environments
Venkatraman Narayanan, Michael Phillips and Maxim Likhachev

Conference Paper, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2012
2017-09-13T10:39:38+00:00