ROSPlan: Planning in the Robot Operating System
Open accessAbstract
The Robot Operating System (ROS) is a set of software libraries and tools used to build robotic systems. ROS is known for a distributed and modular design. Given a model of the environment, task planning is concerned with the assembly of actions into a structure that is predicted to achieve goals. This can be done in a way that minimises costs, such as time or energy. Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state. Moreover, planning can be used in less constrained domains to provide more intelligent behaviour. This paper describes the ROSP LAN framework, an architecture for embedding task planning into ROS systems. We provide a description of the architecture and a case study in autonomous robotics. Our case study involves autonomous underwater vehicles in scenarios that demonstrate the flexibility and robustness of our approach.
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ROSPlan is not designed for fast reaction to dynamic environmental changes caused by external events, and it would not work when the initial state is only partially observable.
“ROSPlan leverages automated planning with PDDL2.1, but it is not designed for fast reaction in case of dynamic changes in the environment caused by external events, and would not work in case of a partially observable initial state.”
From Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics (2023) · cites this paper as [41]
The Case study (section 4; page not provided) says the AUV began without knowing the tank size, obstacles, or valve-panel location. The Outcome section reports that discovering the panel canceled the current action and prompted replanning, contrary to the claim that ROSPlan would not work with an incompletely known initial state or adapt to environmental changes. The paper does not establish a measured speed of reaction. (LLM, conf=0.86)
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Cited by
Sorted by citations
Applications and Challenges of Artificial Intelligence in Space Missions
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“Examples of such middleware on which frameworks can be built upon include (STRANDS [229]) for long-term navigation planning & task scheduling; (ROSPlan [230]) for planning and execution and (RoboSherlock [231]) for knowledge-enabled perception.”
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- Authors
- Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carrera, A., Palomeras, N., Hurtos, N., & Carreras, M.
- Affiliations
- King's College London (GB)
- Universitat de Girona (ES)
- Venue
- Proceedings of the International Conference on Automated Planning and Scheduling
- Published
- 2015-04-08
- Volume
- 25
- Pages
- 333-341
- ISSN
- 2334-0835 (ISSN-L)
- 2334-0843
- OpenAlex
- W2972154709
- Cited by
- 245
- References
- 34
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 24 · 2023: 34 · 2022: 24 · 2021: 38 · 2020: 30 · 2019: 23 · 2018: 18 · 2017: 12 · 2016: 6 · 2015: 3
- FWCI
- 7.33
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International Conference on Automated Planning and Scheduling (publishedVersion)
- Universitat de Girona Digital Repository (Universitat de Girona) (acceptedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- Universitat de Girona Digital Repository (Universitat de Girona) (submittedVersion, public-domain)
- Research Portal (King's College London) (submittedVersion, other-oa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- Robot Operating System, task planning, planning architecture, autonomous underwater vehicles, robots, planning under uncertainty
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