Distributed Situational Awareness in Robot Swarms

Many‐robot systems are becoming a reality for large companies that can invest in bespoke solutions. These systems often require carefully engineered infrastructure and a central planner to coordinate the robots. Outside these controlled environments, robots typically generate a shared situational awareness of the world and state of their task. This requires sophisticated mapping, perception, and control, with changes to the environment or tasks causing challenges to robot deployment. The assumption that centralized situational awareness is needed to deal with real‐world complexity may be holding back the field from deploying many‐robot systems. Yet potential applications are wide‐ranging, including environmental monitoring, construction, agriculture, and logistics. Mainstream adoption requires usability out‐of‐the‐box, in unstructured environments, at a reasonable cost. Distributed situational awareness is proposed as a method to design many‐robot systems differently. Distributed situational awareness allows swarms of low‐cost robots to rapidly and accurately capture the state of an environment and act accordingly, with no central data storage, modeling, or control. Its distributed nature enhances resilience and redundancy while reducing reliance on infrastructure and central planners. Deploying distributed situational awareness however requires new tools to design hardware and algorithms, demonstrate that it works and is safe, and is intuitive for users of the swarm.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Distributed Situational Awareness in Robot Swarms ; volume:2 ; number:11 ; year:2020 ; extent:5 ; Advanced intelligent systems ; 2, Heft 11 (2020) (gesamt 5)

Contributors, Places and Time

• Creator: Jones, Simon ; Milner, Emma ; Sooriyabandara, Mahesh ; Hauert, Sabine

Further information

• DOI: 10.1002/aisy.202000110
• URN: urn:nbn:de:101:1-2022062910352734692009
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:24 AM CEST