8,814 research outputs found
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Active matter clusters at interfaces
Collective and directed motility or swarming is an emergent phenomenon
displayed by many self-organized assemblies of active biological matter such as
clusters of embryonic cells during tissue development, cancerous cells during
tumor formation and metastasis, colonies of bacteria in a biofilm, or even
flocks of birds and schools of fish at the macro-scale. Such clusters typically
encounter very heterogeneous environments. What happens when a cluster
encounters an interface between two different environments has implications for
its function and fate. Here we study this problem by using a mathematical model
of a cluster that treats it as a single cohesive unit that moves in two
dimensions by exerting a force/torque per unit area whose magnitude depends on
the nature of the local environment. We find that low speed (overdamped)
clusters encountering an interface with a moderate difference in properties can
lead to refraction or even total internal reflection of the cluster. For large
speeds (underdamped), where inertia dominates, the clusters show more complex
behaviors crossing the interface multiple times and deviating from the
predictable refraction and reflection for the low velocity clusters. We then
present an extreme limit of the model in the absense of rotational damping
where clusters can become stuck spiraling along the interface or move in large
circular trajectories after leaving the interface. Our results show a wide
range of behaviors that occur when collectively moving active biological matter
moves across interfaces and these insights can be used to control motion by
patterning environments.Comment: 15 pages, 7 figure
Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms
This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm
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