102 citations · 308 across the 8 of their papers we have counts for
8 papers
Risk-aware Integrated Task and Motion Planning for Versatile Snake Robots under Localization Failures
Ashkan Jasour, Guglielmo Daddi, Masafumi Endo +7
Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots…
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…
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
Valentin N. Hartmann, Marlin P. Strub, Marc Toussaint +1
Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across…
AIT* and EIT*: Asymmetric bidirectional sampling-based path planning
Marlin P. Strub, Jonathan D. Gammell
Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their searc…
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…
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…