Experimenting with robotic intra-logistics domains
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Abstract (arXiv)
We introduce the asprilo [1] framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return, asprilo allows users to study alternative solutions as regards effectiveness and scalability. Although asprilo relies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely, asprilo consists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites. [1] asprilo stands for Answer Set Programming for robotic intra-logistics
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Apr 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 0; 2025: 4; 2026: 1.
12 papers.
- 2022Answer Set Planning: A Surveycited by 12
- 2021Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidancecited by 8
- 2023Logic programming for deliberative robotic task planningcited by 8
- 2025Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)cited by 5
- 2023Clingraph: A System for ASP-based Visualizationcited by 4
- 2021Automata for dynamic answer set solving: Preliminary reportcited by 1
- 2023Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alterationcited by 1
- 2025SM-based Semantics for Answer Set Programs Containing Conditional Literals and Arithmeticcited by 1
- 2021Automata Techniques for Temporal Answer Set Programmingcited by 0
- 2025Relating Answer Set Programming and Many-sorted Logics for Formal Verificationcited by 0
- 2025Diminution: On Reducing the Size of Grounding ASP Programscited by 0
- 2026GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environmentscited by 0
- 2026GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments11 Mar 2026
- 2025SM-based Semantics for Answer Set Programs Containing Conditional Literals and Arithmetic3 Nov 2025
- 2025Diminution: On Reducing the Size of Grounding ASP Programs12 Aug 2025
- 2025Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)3 Mar 2025
- 2025Relating Answer Set Programming and Many-sorted Logics for Formal Verification13 Feb 2025
- 2023Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration11 Aug 2023
- 2023Clingraph: A System for ASP-based Visualization17 Mar 2023
- 2023Logic programming for deliberative robotic task planning18 Jan 2023
- 2022Answer Set Planning: A Survey11 Feb 2022
- 2021Automata Techniques for Temporal Answer Set Programming17 Sep 2021
- 2021Automata for dynamic answer set solving: Preliminary report4 Sep 2021
- 2021Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance23 May 2021