Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic

Shah, Brual C.; źVec, Petr; Bertaska, Ivan R.; Sinisterra, Armando J.; Klinger, Wilhelm B.; Ellenrieder, Karl D. von; Dhanak, Manhar R.; Gupta, Satyandra K.

doi:10.1007/s10514-015-9529-x

Cited by 61 publications

(27 citation statements)

References 77 publications

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“…Second, object avoidance is an extremely important aspect of unmanned operations and becomes an absolute requirement when operating in existing complex manned boat traffic. Adaptive evaluation schemes and algorithms have been recently developed and tested to allow autonomous watercraft to meet international regulations for preventing collisions at sea ( Kuwata et al, 2014 ; Shah et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV)

Powers

Hanlon

Schmale

2018

PeerJ

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Microorganisms are ubiquitous in freshwater aquatic environments, but little is known about their abundance, diversity, and transport. We designed and deployed a remote-operated water-sampling system onboard an unmanned surface vehicle (USV, a remote-controlled boat) to collect and characterize microbes in a freshwater lake in Virginia, USA. The USV collected water samples simultaneously at 5 and 50 cm below the surface of the water at three separate locations over three days in October, 2016. These samples were plated on a non-selective medium (TSA) and on a medium selective for the genus Pseudomonas (KBC) to estimate concentrations of culturable bacteria in the lake. Mean concentrations ranged from 134 to 407 CFU/mL for microbes cultured on TSA, and from 2 to 8 CFU/mL for microbes cultured on KBC. There was a significant difference in the concentration of microbes cultured on KBC across three sampling locations in the lake (P = 0.027), suggesting an uneven distribution of Pseudomonas across the locations sampled. There was also a significant difference in concentrations of microbes cultured on TSA across the three sampling days (P = 0.038), demonstrating daily fluctuations in concentrations of culturable bacteria. There was no significant difference in concentrations of microbes cultured on TSA (P = 0.707) and KBC (P = 0.641) across the two depths sampled, suggesting microorganisms were well-mixed between 5 and 50 cm below the surface of the water. About 1 percent (7/720) of the colonies recovered across all four sampling missions were ice nucleation active (ice+) at temperatures warmer than −10 °C. Our work extends traditional manned observations of aquatic environments to unmanned systems, and highlights the potential for USVs to understand the distribution and diversity of microbes within and above freshwater aquatic environments.

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Section: Introductionmentioning

confidence: 99%

Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV)

Powers

Hanlon

Schmale

2018

PeerJ

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“…For instance in [11], goal hypotheses of road driving obstacles are formed based on the current road topology, and a probabilistic motion model is used to predict their future trajectories conditioned on the hypotheses. However, for maritime applications, it is to the author's knowledge only [12] and [13] that considers nearby obstacles to be agents capable of different maneuvers or intentions. In [12], an A* search method is applied to collision free path planning which penalizes high collision risk, traffic rule violations and path deviation.…”

Section: Introductionmentioning

confidence: 99%

Risk-based Autonomous Maritime Collision Avoidance Considering Obstacle Intentions

Tengesdal

Johansen

Brekke

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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A robust and efficient Collision Avoidance (COLAV) system for autonomous ships is dependent on a high degree of situational awareness. This includes inference of the intent of nearby obstacles, including compliance with traffic rules such as COLREGS, in order to enable more intelligent decision making for the autonomous agent. Here, a generalized framework for obstacle intent inference is introduced. Different obstacle intentions are then considered in the Probabilistic Scenario-Based Model Predictive Control (PSB-MPC) COLAV algorithm using an examplatory intent model, when statistics about traffic rules compliance and the next waypoint for an obstacle are assumed known. Simulation results show that the resulting COLAV system is able to make safer decisions when utilizing the extra intent information.

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“…Therefore, researchers have sought smoothing technologies, such as splines, polynomials, and the Bézier curve [15], [16]. There are also lattice-based planners that use control model primitives to search in state space [17], [18]. Such planners are suitable for complex scenarios, but have a relatively low re-planning rate due to computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Such planners are suitable for complex scenarios, but have a relatively low re-planning rate due to computational complexity. To reduce the computation time, a preferred solution is to use hierarchical approaches [18], where a high-level planner, such as the designed planner in this paper, runs at a low re-planning frequency to search for the optimal trajectory within the entire domain, followed by a high-frequency low-level planner to deal with the local changes in the environment [19]. For those path planning problems that can be represented as a set of parameters, evolutionary methods including generic algorithm (GA) [20] and particle swarm optimization [21], plus reinforcement learning [22], are applicable.…”

Section: Introductionmentioning

confidence: 99%

Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters

Hildre

Zhang

2020

IEEE J. Oceanic Eng.

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Ship intelligence has been a hot topic in recent years. How to achieve autonomous maneuvers in a complex marine environment in a safe, efficient, and low-cost manner is a fundamental task that ocean engineers face. This paper presents a two-stage trajectory planning scheme to address the minimumtime maneuvering problem in close-range encounters. The scheme is robust and versatile, as it can deal with the complex spatial variability, such as sea current, state constraints, marine traffic, and physical constraints, of close-range maneuvering. In the first stage, a directed graph with variable length is generated according to the sea current distribution. A wavefront search is applied on the graph to explore the reachability, the cost of state constraints, and the risk of collision. After a discrete solution has been found, the second stage involves searching for a smooth solution. A Bézier curve based parameter optimization approach is proposed to get rid of limited moving directions in the directed graph and explore around the discrete path. The result will be a near-optimal, smooth path. The proposed scheme has been tested to solve the Zermelo's ship steering problem and several other close-range maneuvering problems. The results demonstrate that the scheme is efficient in generating smoothed minimum-time trajectories for surface vessels when maneuvering in close-range encounters.

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Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic

Cited by 61 publications

References 77 publications

Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV)

Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV)

Risk-based Autonomous Maritime Collision Avoidance Considering Obstacle Intentions

Toward Time-Optimal Trajectory Planning for Autonomous Ship Maneuvering in Close-Range Encounters

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