Synchronous Rendezvous for Heterogeneous Robotic Sensor Networks in Geophysical Flows
Abstract
This work leverages the complimentary mobility and sensing capabilities of a network of heterogeneous robots that operate in remote" oceanic environments, to efficiently collect information and effectively manage the volume of data collected. We consider the deplo"yment of minimally actuated active drifters or similarly power-constrained mobile sensors. The active drifters periodically offload their sensory information to more capable robotic vehicles routed in a coordinated fashion through the drifter ensemble. Different" from their passive counterparts, active drifters can adapt, albeit in a limited fashion, their sampling strategies to maximize info""rmation gain. When coupled with more capable autonomous surface, underwater, or even remotely operated vehicles (ASVs, AUVs, or ROVs""), active drifters can significantly increase the spatial sampling reach of ASVs, AUVs, and ROVs. On the other hand, ASVs, AUVs, and" ROVs can complement the sensing capabilities of active drifters since they have larger sensor payloads and reach regions not easily" accessible to the active drifters due to actuation limitations. However, due to their severely limited power budgets, active drifte"rs must have the ability to plan and execute energy aware motion control and coordination strategiesfor data harvesting and rendezvous with AVUs and ASVs for data exchange and upload.The goal is to develop motion planning and control strategies for teams of mobi"le sensors withlimited actuation capabilities or power budgets, i.e., active drifters, to harvest data and rendezvous with other au""tonomous vehicles. The proposed paradigm maximizes the impact of small, power constrained mobile sensors by leveraging the surroundi""ng environmental dynamics to reduce their energy requirements. Towards this end, the objectives of this work are (i) development of" energy-aware motion control strategies for synchronous rendezvous by leveraging the dynamics of the fluidic environment; (ii) synthesis of distributed synchronous rendezvous strategies for a set of moving rendezvous points whose motions are dictated by surrounding flow field; (iii) development of a stochastic modeling and control framework where the individual average behavior can be specified and tuned to achieve the desired collective targets; and (iv) experimental validation of the proposed strategies using the multi-r"obot Coherent Structure Testbed (mCoSTe), an ONR funded research instrument.The novelty of this work lies in the synthesis of ideas"" from nonlinear dynamical systems theory,transport theory, and robotics to develop a sensing and control framework for collaborativ"e unmanned systems operating in dynamic and uncertain environments. Our thesis is that motion planning and control strategies for robotic sensor networks must account for dynamics of the geophysical flow which affect their motion. This proposal is submitted for th"e purpose of transferring an existing award from Drexel University to the University of Pennsylvania, the new home institution for P""I Hsieh. As such, the proposed work will focus on the completion ofthe remaining tasks outlined in the original proposal submitted"" on September 30, 2015.Success of these endeavors will improve the autonomy and energy efficiency of various Navalplatforms, direc""tly affect the Navy s abilities to navigate the oceans, increase the energy-efficiency of existing robotic sensor networks, and prov"ide greater situational awareness for Naval applications. The research will focus on developing a general stochastic control framework for coordinated energy-aware motion planning and navigation that are important for power constrained unmanned systems. The expected outcomes include: (i) energy aware motion planning and control strategies for minimally actuated autonomous vehicles with limited power budgets; (ii) development of new stochastic control tools to enable large collectives of autonomous sensing resources to rend"ezvous in dynamic enviro
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 07, 2017
- Source ID
- N000141712690
Entities
People
- Mong-ying Hsieh
Organizations
- Office of Naval Research
- United States Navy
- University of Pennsylvania