Gradual Collective Upgrade of a Swarm of Autonomous Buoys for Dynamic Ocean Monitoring

Abstract

Swarms of autonomous surface vehicles equipped with environmental sensors and decentralized communications bring a new wave of attractive possibilities for the monitoring of dynamic features in oceans and other waterbodies. However, a key challenge in swarm robotics design is the efficient collective operation of heterogeneous systems. We present both theoretical analysis and field experiments on the responsiveness in dynamic area coverage of a collective of 22 autonomous buoys, where 4 units are upgraded to a new design that allows them to move 80% faster than the rest. This system is able to react on timescales of the minute to changes in areas on the order of a few thousand square meters. We have observed that this partial upgrade of the system significantly increases its average responsiveness, without necessarily improving the spatial uniformity of the deployment. These experiments show that the autonomous buoy designs and the cooperative control rule described in this work provide an efficient, flexible, and scalable solution for the pervasive and persistent monitoring of water environments.

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

“…This area coverage task was expanded on by Vallegra et al (2018), Zoss et al (2018) who demonstrated the use of an physical MRS system for a dynamic area coverage problem: i.e., covering an area that changed shape over time. To achieve this, they augmented inter-agent repulsion together with a potential field gradient attracting agents outside the designated monitoring area towards the area perimeter.…”

Design innovation of mesoscale robotic swarms: applications to cooperative urban sensing and mapping

“…13, using the exploration cooperative control strategy, the mesoscale robotic swarm can diverge favorably even when all of them start from the center of the space and cover the required spaces to be mapped within 3-4 min depending on the dynamic obstacles encountered. Kit et al (2019) 12 Vallegra et al (2018) 22 Zoss et al (2018) 45 Chamanbaz et al (2017) 45…”

“…Hence, this requires only short-range local communications. Nonetheless, during the development phase and experimentation, it is often beneficial to have a more refined control system, and this explains our use of a monitoring station connected through the ad-hoc network to the swarm for our multi-floor Compact components: For the purpose of miniaturizing and increasing system performance, reduce the size and mass of components through optimization and new technology development Niu et al, 2014;Singh et al, 2009;Ajay et al, 2015;Weaver et al, 2010Dharmawan et al, 2018aSundram et al, 2018 Low power consumption: To achieve system performance in terms of duration and longevity, reduce power consumption of components, subsystems, and the overall systems through optimization, elimination of leakage or unnecessary functionality, and intelligent energy management Kit et al, 2018;Qureshi et al, 2006;Nguyen et al, 2018;Keese et al, 2007;Dharmawan et al, 2019aTilstra et al, 2015 Modularity: For the purposes of system flexibility and reconfiguration, localize or increase the modularity of the system by: (1) separating modules to carry out functions that are not closely related; (2) confining functions to single modules; (3) confining functions to as few unique components as possible; (4) dividing modules into multiple small and identical modules; (5) collecting components which are not anticipated to change in time into separate modules; (6) collecting parts that perform functions associated with the same energy domain into separate modules Hariri et al, 2018;Stone et al, 2000;Kit et al, 2019Qureshi et al, 2006Keese et al, 2007;Singh et al, 2009;Weaver et al, 2010;Tilstra et al, 2015 Collaborative swarm: To increase the scalability and performance profile of mesoscale robotic systems, develop decentralized communication in a distributed network and adopt cooperative control by sending and receiving relevant data used by a swarm to produce a host of collective actions Chamanbaz et al, 2017;Zoss et al, 2018 Heterogeneity: For the purpose of adaptability, develop system alternatives or complementary architectures with diversification in states, functionality, or reconfigurability Vallegra et al, 2018;Kit et al, 2019 Design process of mesoscale robotic systems Parallel systems testbed & p...…”

“…Being adaptive further elevates the merit of any robotic system. As the size constraint confines the functionality of a single miniature robot, having a heterogeneous MRS provides the added advantage (Vallegra et al, 2018) of adaptability through collective behavior by distributing diverse responsibilities to distinct robot species.…”

Decentralized Multi-Floor Exploration by a Swarm of Miniature Robots Teaming with Wall-Climbing Units

“…For example in [9], a decentralized algorithm was used to localize a flock of robotic sensor networks. Environmental monitoring tasks were performed by a decentralized swarm of robots in [10]- [12], including with heterogeneous swarms [13]. Decentralized exploration and mapping of unknown indoor entity was demonstrated with a pair of autonomous quadcopters in [14].…”