42 citations · 62 across the 2 of their papers we have counts for
10 papers
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Wil Thomason, Marlin P. Strub, Jonathan D. Gammell
High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms sol…
Admissible heuristics for obstacle clearance optimization objectives
Marlin P. Strub, Jonathan D. Gammell
Obstacle clearance in state space is an important optimization objective in path planning because it can result in safe paths. This technical report presents admissible solution- a…
Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation
Rowan Border, Jonathan D. Gammell
The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the num…
Asymptotically Optimal Sampling-Based Motion Planning Methods
Jonathan D. Gammell, Marlin P. Strub
Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations…
Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
Marlin P. Strub, Jonathan D. Gammell
Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and u…
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
Marlin P. Strub, Jonathan D. Gammell
Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approach…