This paper presents a novel approach to swarm navigation that combines hierarchical abstractions, flocking behaviors, and an efficient collision avoidance mechanism. Our main objective is to keep large groups of robots segregated while safely navigating in a shared environment. For this, we propose the Virtual Group Velocity Obstacle, which is an extension of the Velocity Obstacle concept for groups of robots. By augmenting velocity obstacles with flocking behaviors and hierarchical abstractions, we are able to navigate robotic swarms in a cohesive and smooth fashion. A series of simulations and real experiments were performed and the results show the effectiveness of the proposed approach.

Abstract-Several natural systems adopt self-sorting mechanisms based on segregative behaviors. Among these, cell segregation is of particular interest since it plays an important role in the formation of tissues, organs, and living organisms. The Differential Adhesion Hypothesis states that cells naturally segregate because of differences in affinity, which lead similar cells to strongly adhere to each other. By exploring this principle, we propose a controller that can segregate a heterogeneous swarm of robots according to the characteristics of each agent, such that similar robots form homogeneous teams and dissimilar robots are segregated. We apply LaSalle's Invariance Principle to show convergence and perform simulated experiments in order to demonstrate the robustness and effectiveness of the proposed controller. Results show that our approach allows a swarm of multiple heterogeneous robots to segregate in a coherent and smooth fashion, without any inter-agent collisions.

The use of large groups of robots in the execution of complex tasks has received much attention in recent years. Generally called robotic swarms, these systems employ a large number of simple agents to perform different types of tasks. A basic requirement for most robotic swarms is the ability for safe navigation in shared environments. Particularly, two desired behaviors are to keep robots close to their kin and to avoid merging with distinct groups. These are respectively called cohesion and segregation, which are observed in several biological systems. In this paper, we investigate two different approaches that allow swarms of robots to navigate in a cohesive fashion while being segregated from other groups of agents.Our first approach is based on artificial potential fields and hierarchical abstractions. However, this method has one drawback: it needs a central entity which is able to communicate with all robots. To cope with this problem, we introduce a distributed mechanism that combines hierarchical abstractions, flocking behaviors, and an efficient collision avoidance mechanism.We perform simulated and real experiments in order to study the feasibility and effectiveness of our methods. Results show that both approaches ensure cohesion and segregation during swarm navigation.

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Abstract-Safe and efficient navigation of robotic swarms is an important research problem. One of the main challenges in this area is to avoid congestion, which usually happens when large groups of robots share the same environment. In this paper, we propose the use of hierarchical abstractions in conjunction with simple traffic control rules based on virtual forces to avoid congestion in swarm navigation. We perform simulated and real experiments in order to study the feasibility and effectiveness of the proposed algorithm. Results show that our approach allows the swarm to navigate without congestions in a smooth and coherent fashion, being suitable for large groups of robots.