Towards Deliberative Control in Marine Robotics

Rajan, Kanna; Py, Frédéric; Barreiro, Javier

doi:10.1007/978-1-4614-5659-9_3

Cited by 26 publications

(23 citation statements)

References 79 publications

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“…These problems are precisely where the applicability of a temporal constrainedbased planning/execution framework like T-REX [9], [10], [11] can be tackled in the near future. While T-REX has already been integrated on our LAUVs, coupling multiple T-REX enabled vehicles to a shorebased mixed-initiative system as in this paper, is future work.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Our previous work is among the few to include automated planning in this harsh domain where we have demonstrated embedded continuous planning and execution in the context of scientific upper watercolumn exploration on a single AUV [9], [10], [11]. Recent work [12] shows more interest along similar lines.…”

Section: Related Workmentioning

confidence: 99%

“…Further, our work is informed by past efforts in mixed-initiative command/control in the space domain; the MAPGEN system continues to command the rover Opportunity on the surface of Mars using automated planning methods [8] also used in this work. While we have demonstrated a form of mixed-initiative control on AUVs, this has been in the context of a single AUV while tracking gradients [11] and where automated decision-making was onboard the vehicle. To the best of our knowledge using automated planning as a means to provide abstraction in control over multiple vehicles in the oceanographic domain is novel.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

On mixed-initiative planning and control for Autonomous underwater vehicles

Chrpa

Pinto

Ribeiro

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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On mixed-initiative planning and control for Autonomous underwater vehicles

Chrpa

Pinto

Ribeiro

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…First is the Gulper water-sampler [5], consisting of ten 2-liter syringes in the midsection of the vehicle. Second, it has an advanced control system which synthesizes abstract plans from shore-based commands, is responsive to failure using in situ plan repair and recovery, and can use a diverse set of sampling techniques which can be dynamically modified from shore [6], [7]. Cued by data-driven models in the ODSS, scientists can alter where, when and how the AUV will take samples.…”

Section: Introductionmentioning

confidence: 99%

Learning-based event response for marine robotics

Bernstein

Graham

Cline

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Robotic vehicles have become a critical tool for studying the under-sampled coastal ocean. This has led to new paradigms in scientific discovery. The combination of agility, reactivity, and persistent presence makes autonomous robots ideal for targeted sampling of elusive, episodic events such as algal blooms. In order to achieve this goal, they need to be deployed at the right place and time. To that end, we have designed and will soon deploy a shore-based event recognition technology to continuously monitor remote sensing imagery for algal blooms as targets for robotic field experiments. A Support Vector Machine underlies a field-tested decision support system which scientists will consult prior to deploying robots in the coastal ocean. Our aim is to target oceanographic field experiments for evaluation and verification.

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“…The role of the T-REX agent then, is to ensure that all the reactors will be able to interact concurrently so that they are informed of state evolution that may impact them, have a sufficient amount of time to synthesize plans and coordinate plan dispatch across reactor boundaries. Details of T-REX are beyond the scope of this paper and can be found in [12], [13], [14]. f) UAVs: Three identical low-cost UAV platforms ( Fig.…”

mentioning

confidence: 99%

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

Faria

Pinto

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted watercolumn sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

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Towards Deliberative Control in Marine Robotics

Cited by 26 publications

References 79 publications

On mixed-initiative planning and control for Autonomous underwater vehicles

On mixed-initiative planning and control for Autonomous underwater vehicles

Learning-based event response for marine robotics

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

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