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 deployment 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 information 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 drifters
must have the ability to plan and execute energy aware motion control and coordination strategies for data harvesting and rendezvous with AVUs and ASVs for data exchange and upload.
This work focuses on developing motion planning and control strategies for teams of mobile sensors with limited actuation capabilities or power budgets, i.e., active drifters, to harvest data and rendezvous with other autonomous vehicles. The proposed paradigm maximizes the impact of small, power constrained mobile sensors by leveraging the surrounding environmental dynamics to reduce their energy requirements. The objectives of this work are:
- To develop energy-aware motion control strategies for synchronous rendezvous by leveraging the dynamics of the fluidic environment;
- To synthesize distributed synchronous rendezvous strategies for a set of moving rendezvous
points whose motions are dictated by surrounding flow field;
- To develop a stochastic modeling and control framework where the individual average behavior can be specified and tuned to achieve the desired collective targets; and
- To validate the proposed strategies using the multi-robot Coherent Structure Testbed (mCoSTe).
Success of these endeavors will improve the autonomy and energy efficiency of various marine
platforms, directly affect the human’s abilities to navigate the oceans, increase the energy-efficiency of existing robotic sensor networks, and provide greater situational awareness for marine, coastal, and littoral applications. The research focuses 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:
- Energy aware motion planning and control strategies for minimally actuated autonomous vehicles with limited power budgets;
- New stochastic control tools to enable large collectives of autonomous sensing resources to rendezvous in dynamic environments; and
- A novel modeling, control, and analysis framework for coordinating large collectives of collaborating autonomous unmanned systems subject to energy constraints.