Programming micro-aerial vehicle swarms with karma

Abstract

Research in micro-aerial vehicle (MAV) construction, control, and high-density power sources is enabling swarms of MAVs as a new class of mobile sensing systems. For efficient operation, such systems must adapt to dynamic environments, cope with uncertainty in sensing and control, and operate with limited resources. We propose a novel system architecture based on a hive-drone model that simplifies the functionality of an individual MAV to a sequence of sensing and actuation commands with no in-field communication. This decision simplifies the hardware and software complexity of individual MAVs and moves the complexity of coordination entirely to a central hive computer. We present Karma, a system for programming and managing MAV swarms. Through simulation and testbed experiments we demonstrate how applications in Karma can run on limited resources, are robust to individual MAV failure, and adapt to changes in the environment.

Novel framework for data collection in wireless sensor networks using flying sensors

“…While a mobile relay could reposition itself in proximity to certain node(s) that are generating higher traffic and thereby assist them in multi-hop traffic relaying [3]. Mobile sensor nodes in the form of MAVs referred as sensor fly have been proposed by the authors in [6]. They are expected to perform a collective sensing function flying as a swarm of nodes for applications such as crowd surveillance and disaster recovery [7].…”

“…They are expected to perform a collective sensing function flying as a swarm of nodes for applications such as crowd surveillance and disaster recovery [7]. It is also stated that for these flying sensor nodes it is the actuation which consumes the maximum amount of energy as is the case for other types of mobile nodes [6]. The research focus has been on making the sensor MAV more intelligent in sensing operations, thereby increasing the possible applications addressable by them.…”

“…Various types of actuation mechanisms to move the mobile nodes between two given locations have been proposed in the literature and include: wheels, springs and wings [4]. Flying sensing nodes in the form of miniature aerial vehicles (MAVs) capable of sensing a given area, aerially, have been discussed in the literature [6].…”

A comparison of deterministic and stochastic approaches for allocating spatially dependent tasks in micro-aerial vehicle collectives

“…We compare the utility of a deterministic task allocation approach using the resource management system Karma [7] and a stochastic approach OptRAD [4], which is based on the optimization of a spatiotemporal model of the swarm population dynamics. These two approaches were developed for swarms of highly resource-constrained robots that must adapt to disturbances and failures.…”

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Abstract-We compare our previously developed deterministic [7] and stochastic [3], [4] strategies for allocating tasks in robotic swarms 1 consisting of very large populations of highly resource-constrained robots. We study our two task allocation approaches in a simulated scenario in which a collective of insect-inspired micro-aerial vehicles (MAVs) must produce a specified spatial distribution of pollination activity over a crop field.

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