5 research outputs found
Gradual Collective Upgrade of a Swarm of Autonomous Buoys for Dynamic Ocean Monitoring
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.Comment: Proceedings of the OCEANS 2018 conferenc
Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys
Autonomous marine environmental monitoring problem traditionally encompasses
an area coverage problem which can only be effectively carried out by a
multi-robot system. In this paper, we focus on robotic swarms that are
typically operated and controlled by means of simple swarming behaviors
obtained from a subtle, yet ad hoc combination of bio-inspired strategies. We
propose a novel and structured approach for area coverage using multi-agent
reinforcement learning (MARL) which effectively deals with the non-stationarity
of environmental features. Specifically, we propose two dynamic area coverage
approaches: (1) swarm-based MARL, and (2) coverage-range-based MARL. The former
is trained using the multi-agent deep deterministic policy gradient (MADDPG)
approach whereas, a modified version of MADDPG is introduced for the latter
with a reward function that intrinsically leads to a collective behavior. Both
methods are tested and validated with different geometric shaped regions with
equal surface area (square vs. rectangle) yielding acceptable area coverage,
and benefiting from the structured learning in non-stationary environments.
Both approaches are advantageous compared to a na\"{i}ve swarming method.
However, coverage-range-based MARL outperforms the swarm-based MARL with
stronger convergence features in learning criteria and higher spreading of
agents for area coverage.Comment: Accepted for Publication at IEEE/MTS OCEANS 202
Decentralized Multi-Floor Exploration by a Swarm of Miniature Robots Teaming with Wall-Climbing Units
In this paper, we consider the problem of collectively exploring unknown and
dynamic environments with a decentralized heterogeneous multi-robot system
consisting of multiple units of two variants of a miniature robot. The first
variant-a wheeled ground unit-is at the core of a swarm of floor-mapping robots
exhibiting scalability, robustness and flexibility. These properties are
systematically tested and quantitatively evaluated in unstructured and dynamic
environments, in the absence of any supporting infrastructure. The results of
repeated sets of experiments show a consistent performance for all three
features, as well as the possibility to inject units into the system while it
is operating. Several units of the second variant-a wheg-based wall-climbing
unit-are used to support the swarm of mapping robots when simultaneously
exploring multiple floors by expanding the distributed communication channel
necessary for the coordinated behavior among platforms. Although the
occupancy-grid maps obtained can be large, they are fully distributed. Not a
single robotic unit possesses the overall map, which is not required by our
cooperative path-planning strategy.Comment: Accepted for publication in IEEE-MRS 2019, Rutgers University, New
Brunswick (NJ), US
Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking
Current strategies employed for maritime target search and tracking are
primarily based on the use of agents following a predetermined path to perform
a systematic sweep of a search area. Recently, dynamic Particle Swarm
Optimization (PSO) algorithms have been used together with swarming multi-robot
systems (MRS), giving search and tracking solutions the added properties of
robustness, scalability, and flexibility. Swarming MRS also give the end-user
the opportunity to incrementally upgrade the robotic system, inevitably leading
to the use of heterogeneous swarming MRS. However, such systems have not been
well studied and incorporating upgraded agents into a swarm may result in
degraded mission performances. In this paper, we propose a PSO-based strategy
using a topological k-nearest neighbor graph with tunable exploration and
exploitation dynamics with an adaptive repulsion parameter. This strategy is
implemented within a simulated swarm of 50 agents with varying proportions of
fast agents tracking a target represented by a fictitious binary function.
Through these simulations, we are able to demonstrate an increase in the
swarm's collective response level and target tracking performance by
substituting in a proportion of fast buoys.Comment: Accepted for IEEE/MTS OCEANS 2020, Singapor
Gradual Collective Upgrade of a Swarm of Autonomous Buoys for Dynamic Ocean Monitoring
© 2018 IEEE. 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