Task Correlation and Edge-Cost Estimation for Robotic Task Sequencing Problem in Torus C-Space
PHAYUTH YONRITH, Ayoung Hong
AI summary
Problem
Existing robotic task sequencing methods typically reduce the problem to a Traveling Salesman Problem in Euclidean space, neglecting the robot's natural toroidal C-space topology and joint limits, which leads to suboptimal paths or motion failures.
Approach
The authors propose a solver that estimates task correlations using nearest-neighbor methods to prune edges, and approximates edge costs via an implicit sparse Random Geometric Graph, ultimately solving a Generalized TSP while respecting toroidal joint configurations.
Key results
- Reduced task sequencing cost from 31.781 to 25.309 in a 2D planar robot experiment
- Successfully leverages full ±2π joint range instead of constrained ±π limits
- Prunes GTSP edges via task correlation and approximates costs using sparse RGG
- Produces collision-free, optimized motion tours through GTSP solving and STOMP refinement
Why it matters
Enables more efficient and feasible motion planning for robotic manipulators in industrial and service applications by respecting their physical joint constraints and non-Euclidean configuration spaces.
Abstract
Robotic manipulators are increasingly used in diverse applications, ranging from industrial automation to human-centered tasks such as grocery picking and packaging, where they are often required to perform sequences of tasks while maintaining motion optimality and collision-free opera- tion over long horizons. This type of problem is known as the Robotic Task Sequencing Problem. Most existing works address this problem by reducing it to the Traveling Salesman Prob- lem (TSP) and within a purely Euclidean framework, neglecting the robot’s inherently non-Euclidean toroidal C-space topology. This simplification limits the selection of feasible configurations and may lead to failure, suboptimal, or detoured motions. In this paper, we propose a robotic task sequencing problem solver that incorporates the robot’s natural T n topology and joint limits.