Controller performance of marine robots in reminiscent oil surveys

Mukhopadhyay, Shayok; Wang, Chuanfeng; Bradshaw, S.M.; Bazie, Valerie; Maxon, Sean; Hicks, L. L.; Patterson, Mark; Zhang, Fumin

doi:10.1109/iros.2012.6385947

Cited by 12 publications

(12 citation statements)

References 20 publications

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“…More specifically, our work is inspired by the curve-tracking problem [36], which is important to the path-following control of autonomous vehicles. Curve tracking controllers have been used for applications such as obstacle avoidance using wheeled robots, and marine sampling [21]. These controllers are robust to real world disturbances, which is justified in [18,19] by using tools like ISS.…”

Section: Figure 1 Figure Showing Unit Circles For Different Normsmentioning

confidence: 99%

An algorithm for computing robust forward invariant sets of two dimensional nonlinear systems

Mukhopadhyay

Zhang

2020

Asian Journal of Control

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Robustness of nonlinear systems can be analyzed by computing robust forward invariant sets (RFISs). Knowledge of the smallest RFIS of a system, can help analyze system performance under perturbations. A novel algorithm is developed to compute an approximation of the smallest RFIS for two-dimensional nonlinear systems subjected to a bounded additive disturbance. The problem of computing an RFIS is formulated as a path planning problem, and the algorithm developed plans a path which iteratively converges to the boundary of an RFIS. Rigorous mathematical analysis shows that the proposed algorithm terminates in a finite number of iterations, and that the output of the proposed algorithm is an RFIS.Simulations are presented to illustrate the proposed algorithm, and to support the mathematical results. This work may aid future development, for use with higher dimensional systems.

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Section: Figure 1 Figure Showing Unit Circles For Different Normsmentioning

confidence: 99%

An algorithm for computing robust forward invariant sets of two dimensional nonlinear systems

Mukhopadhyay

Zhang

2020

Asian Journal of Control

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“…Mukhopadhyay et al [104] use a twin-hull catamaran named ASV-Victoria to perform autonomous surveys in regions polluted by crude oil. They focus on developing a controller to enable the robots to follow lines and curves and maintain formation collectively while measuring reminiscent crude oil along their paths.…”

Section: Use Of Usvs In Environmental Disasters With Contaminantsmentioning

confidence: 99%

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

141

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Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

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“…It readily follows that the new delay bound (81)τ =c 1c2 2 √σ L converges to +∞ as κ n → 0 − and κ g → 0 − , so there is a tradeoff between the magnitudes of the curvatures and the upper bounds on the admissible input delays. While our ability to compensate for arbitrarily long input delays is interesting from the theoretical standpoint, our marine robotics field work [29,30] suggests that while it is important to compensate for delays, the delays are generally of the order of 1 or 2 seconds, so severe restrictions on the magnitudes of the curvatures are not necessary. This also makes it possible to apply our results to a broad class of possible 3D curves.…”

Section: Adaptive Iss Tracking With Input Delaysmentioning

confidence: 99%

Robustness of Adaptive Control under Time Delays for Three-Dimensional Curve Tracking

Malisoff¹,

Zhang²

2015

SIAM J. Control Optim.

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We analyze the robustness of a class of controllers that enable three-dimensional curve tracking by a free moving particle. The free particle tracks the closest point on the curve. By building a strict Lyapunov function and robustly forward invariant sets, we show input-to-state stability under predictable tolerance and safety bounds that guarantee robustness under control uncertainty, input delays, and a class of polygonal state constraints, including adaptive tracking and parameter identification under unknown control gains. Such an understanding may provide certified performance when the control laws are applied to real-life systems. Introduction.Curve tracking is a central problem in control and path planning for mobile robots [1,8,27,36,37]. The work presented in [39] used a nonstrict Lyapunov function to find feedback controllers that ensure that a robot moves parallel to a two-dimensional (2D) curve. See also [32] for chained form systems that capture the nonholonomic dynamics of a large variety of mobile robots, such as a kinematic car with trailers, which led to extended curve tracking control laws, and [12] for a reformulation of 2D curve tracking in the three-dimensional (3D) case. Curve tracking is also important for cooperative controllers that track motion for multiple mobile robots; see [38] for applications to ocean sensing. The controls in the works cited above have been found to give reliable performance, even under severe perturbations, in farming [18], obstacle avoidance in corridors [41], and ocean sampling [38].Given a curve tracking control, we are interested in deriving predictable safety and tolerance bounds under several types of uncertainty and input delays, and in identifying unknown control gains. To this end, this paper presents novel 3D analogs of the robustness results that we derived for 2D in [20,21]. For 2D tracking, [20] proved robustness using input-to-state stability (ISS) with respect to actuator errors and robust forward invariance, which gave predictable tolerance and safety bounds under time delays and a class of polygonal state constraints. Briefly described, robust forward invariance means forward invariance in the usual sense of differential equations, but that is also preserved under perturbations; see section 3 for the precise definition. Actuator errors can be modeled as additive perturbations on controls, and communication delays are also common [6, 16], e.g., in marine robotics where there may only be intermittent communications under unfriendly sea conditions. In

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Controller performance of marine robots in reminiscent oil surveys

Cited by 12 publications

References 20 publications

An algorithm for computing robust forward invariant sets of two dimensional nonlinear systems

An algorithm for computing robust forward invariant sets of two dimensional nonlinear systems

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Robustness of Adaptive Control under Time Delays for Three-Dimensional Curve Tracking

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