Swarm SLAM: Challenges and Perspectives

Abstract

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

Towards Fully Autonomous UAVs: A Survey

“…Other applications where multi-UAV systems were deployed include distributed target search and tracking [ 336 , 337 , 338 , 339 ], distributed monitoring and surveillance [ 340 , 341 , 342 , 343 ] and cooperative mapping [ 344 , 345 ]. The research on cooperative mapping in unknown environments, or swarm SLAM, is is not mature yet with not enough established methodologies according to [ 346 ], which motivates more research in this promising area.…”

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

“…Moreover, many ambitious applications remain for multi-robot systems, such as the exploration of other planets (Vitug, 2021;Ebadi et al, 2021). To reach those moonshot goals, ongoing trends in the research community aim to push the boundaries of multi-robot systems towards increasingly larger teams, or swarms of robots (Beni, 2004;Kegeleirs et al, 2021), which potentially allow parallel operations that are more efficient and versatile. However, this is still largely uncharted territory since current multi-robot applications either involve very few robots or rely upon large amounts of centralized computation in server clusters.…”

Shepherding algorithm for heterogeneous flock with model-based discrimination

“…The guidance and navigation of flocks of agents have several applications including guiding birds away from runways for preventing bird strikes [1], collecting oil spills in oceans and rivers [2,3], and navigating a swarm of robots for map creation [4] and coverage [5]. For such systems, a variety of guidance methods for flocks have been proposed in the literature.…”