PlanSys2: A Planning System Framework for ROS2
Not on the map. Why?
Abstract (arXiv)
Autonomous robots need to plan the tasks they carry out to fulfill their missions. The missions' increasing complexity does not let human designers anticipate all the possible situations, so traditional control systems based on state machines are not enough. This paper contains a description of the ROS2 Planning System (PlanSys2 in short), a framework for symbolic planning that incorporates novel approaches for execution on robots working in demanding environments. PlanSys2 aims to be the reference task planning framework in ROS2, the latest version of the em de facto standard in robotics software development. Among its main features, it can be highlighted the optimized execution, based on Behavior Trees, of plans through a new actions auction protocol and its multi-robot planning capabilities. It already has a small but growing community of users and developers, and this document is a summary of the design and capabilities of this project.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 3 of its 31 references that are in the map. First posted on arXiv 1 Jul 2021.
Builds on
3 of 31 references.
References · 3
- 2017Behavior Trees in Robotics and AI: An Introductioncited by 107
- 2020The Marathon 2: A Navigation Systemcited by 70
- 2021Optimized Execution of PDDL Plans using Behavior Treescited by 4
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 3; 2026: 2.
6 papers.
- 2023When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applicationscited by 2
- 2026Jointly Learning Predicates and Actions Enables Zero-Shot Skill Compositioncited by 1
- 2025ROSA: A Knowledge-based Solution for Robot Self-Adaptationcited by 0
- 2025Model-Based AI planning and Execution Systems for Roboticscited by 0
- 2025Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOMcited by 0
- 2026APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Constructioncited by 0
- 2026APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction13 Oct 2026
- 2026Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition20 May 2026
- 2025Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM15 Jul 2025
- 2025Model-Based AI planning and Execution Systems for Robotics7 May 2025
- 2025ROSA: A Knowledge-based Solution for Robot Self-Adaptation29 Apr 2025
- 2023When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications26 Sep 2023