Obstacle Avoidance for Unmanned Sea Surface Vehicles: A Hierarchical Approach

Krishnamurthy, P.; Khorrami, Farshad; Ng, Tzer Leei

doi:10.3182/20080706-5-kr-1001.01152

Cited by 13 publications

(14 citation statements)

References 15 publications

(9 reference statements)

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“…It shows 27 possible paths (N σ = 27) a mover can select. The first branch (Λ 1 ) consists of three trajectories N Λ1 = 3, Λ 1 = δ (1,1) , δ (1,2) , δ (1,3) , as well as the other decision bases concatenated at the ends of each tier of trajectories. N T = 13, is the total number of decision bases.…”

Section: Decision Tree Formulationmentioning

confidence: 99%

First-Order Short-Range Mover Prediction Model (SRMPM)

Overstreet

Khorrami

2011

Proceedings of the 2011 American Control Conference

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An important problem for intelligent autonomous mobile systems/agents is the ability to predict the motions of other objects/agents. This has natural extensions to cooperative behavior control, where mobile agents avoid each other by predicting the other's motion. In this paper, we have formulated a spatial probability distribution for moving objects with respect to First-Order predictions, which take into account mobility characteristics and how they relate to probable motion. This is a novel method since the most common approach uses Kalman Filters to estimate future states based upon observed previous states only, assuming a geospatial 2-D Gaussian distribution with monolithic variances in both the normal and tangential directions of motion. Unlike prior approaches, our methodology takes into consideration specific dynamic constraints (e.g., Ackermann Steering), and probable decision making capabilities of the mover. By adding higher levels of fidelity to prediction models, more accurate and precise object tracking, avoidance, or engagement can be accomplished with already developed techniques.

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Section: Decision Tree Formulationmentioning

confidence: 99%

First-Order Short-Range Mover Prediction Model (SRMPM)

Overstreet

Khorrami

2011

Proceedings of the 2011 American Control Conference

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“…Xie et al [7] proposed a method of the obstacle avoidance planning of USV based on improved artificial potential field; this method overcomes the problems of local minimum, destination unreachable, and poor accuracy of algorithm by using the traditional artificial potential field. Larson et al [8], Bandyophadyay et al [9], and Krishnamurthy et al [10] used behaviour-based reactive obstacle avoidance approaches to deal with the local obstacle avoidance for USV. Tang et al [11] developed a behaviour-based reactive local obstacle avoidance algorithm based on a dynamic window.…”

Section: State Of the Artmentioning

confidence: 99%

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Wang¹,

Mao²,

Du³

2016

Int. j. simul. model.

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Unmanned surface vehicle (USV) is an important autonomous marine vehicle. The safe navigation of USV is directly determined by the local obstacle avoidance because it must avoid real-time local obstacle in the global path planning in a three-dimensional environment. Therefore, efficient algorithms of real-time local obstacle avoidance for USV are a critical issue. In this study, a new threelayered architecture for local obstacle avoidance algorithm was proposed to solve the local obstacle avoidance problem. First, real environment and obstacle models were established in the polar coordinate. The known static path-planning method was conducted based on particle swarm optimization (PSO). Second, the method was integrated with marine rules based on PSO. Third, an obstacle avoidance method under unknown environment was created based on rolling windows. Finally, a simulation experimental platform was developed to verify the feasibility and effectiveness of the aforementioned measure. Result shows that the proposed algorithm can effectively avoid local obstacles of USV at a computation time of less than 2 s. The USV avoids the obstacles smoothly and reaches the desired destination with complex requirements. The simulation results also demonstrate the promising application of the proposed method in studying the path planning of USV. This method can address the issues of real-time local obstacle avoidance of USV.

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“…Furthermore, such navigation system is mainly determined by obstacle avoidance during navigation. Aiming at the obstacle avoidance of USVs, the hierarchical strategy-based obstacle avoidance approaches have been proposed for USVs; these approaches are composed of global path planning and the local reactive obstacle avoidance algorithm (Larson et al, 2007(Larson et al, , 2006Krishnamurthy et al, 2008;Casalino et al, 2009). Some researchers proposed a navigation rule for the motion obstacle avoidance of USVs in accordance with the Coast Guard Collision Regulations, and the navigation rule was verified by USV kayaks in a real water environment (Benjamin et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the results in Naeem et al (2012) were verified only by simulation merely. Larson et al (2007Larson et al ( , 2006, Bandyophadyay et al (2009) and Krishnamurthy et al (2008)) used behaviorbased reactive obstacle avoidance approaches to deal with local obstacle avoidance for USVs. Larson et al (2007Larson et al ( , 2006 used the Morphin algorithm (Simmons et al, 1998) which was proposed by Carnegie Mellon University, to deal with the local static obstacle avoidance of USVs.…”

Section: Introductionmentioning

confidence: 99%

“…Tang et al (2011) developed a behavior-based reactive local obstacle avoidance algorithm based on a dynamic window. A novel type of obstacle avoidance method called GODZILA was proposed for USVs, and the performance of this method was verified only by a simulation platform in a laboratory (Krishnamurthy et al, 2008). Bandyophadyay et al (2009) proposed a type of reactive obstacle avoidance algorithm for USVs.…”

Section: Introductionmentioning

confidence: 99%

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