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Journal ArticleDOI

A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents

Yogang Singh1, Sanjay Sharma1, Robert Sutton1, DC Hatton1, Asiya Khan1 
01 Dec 2018-Ocean Engineering (Elsevier)-Vol. 169, pp 187-201

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AbstractEven after embargo period expires, authors' right to distribute as green open access is conditional on the green open access version including a DOI link, and on the green open access version being distributed under the Creative Commons CC-BY-NC-ND licence. In addition, the authors' right to distribute as green open access extends only to the author-generated post-print, not to any version with Elsevier typography.

Summary (5 min read)

1. Introduction

  • Recent advances in electronic navigation and intelligent robots have become an imperative aid to navigate marine vehicles effectively for applications ranging from reconnaissance in hostile areas to operations in dangerous weather conditions (Loe, 2008).
  • Ocean environmental effects and moving obstacles play the most significant role in path planning of USVs and very few literatures have covered their effect on path planning in the last decade (Tam et al., 2009, Statheros et al., 2008).
  • In the subsequent section, simulation studies conducted in various environmental scenarios are reported and the proposed approach is benchmarked.
  • The conclusions of the current study are reported in the final section.

1.2. Problem definition and major contribution

  • In the present context of autonomy required in the marine environment, autonomous navigation of USVs in a practical marine environment needs to be cognizable of three important issues, namely, safety, reliability of the mission and likelihood of the success (Statheros et al., 2008, LaValle, 2006).
  • Central to the path planning algorithms, two approaches are widely adopted namely, a waypoint approach and a trajectory based approach.
  • Hence there is a challenge to conserve energy as well as consider safety of USVs in USV path planning for USVs designed with heterogeneous mission requirements in mind.
  • Ocean environmental effects can be bifurcated into three streams, as the additive and multiplicative disturbances on vehicle hull, namely, wind, waves and ocean currents (Fossen, 1995).
  • Another major challenge is to understand the steady non uniform headwind and tailwind (Knight, 2008, Belcher, 2007) currents effect on way-point generation and optimality in grid-based path planners.

2.1. Environmental mapping

  • The abstraction of path planning for USVs is shown in Fig.3.
  • In order to implement path planning algorithms, mapping the environment becomes the initial step.
  • Environmental mapping converts world space into Configuration space which helps in quick implementation of algorithms and manageable storage in computers (Mooney et al., 2010).
  • Portsmouth harbour is among the busiest harbours in United Kingdom and is a perfect area for understanding path planning of USVs.

2.2. A* Algorithm

  • In the present study, the A* approach with safety distance constraints has been adopted.
  • Since the current study considers an USV, Springer, developed with primary purpose of monitoring sea pollution, generation of safer way points with conservation of optimality for higher endurance becomes the highest priority.
  • No approach has been able to compute path with a better computational time than the conventional A* approach in simulation studies.
  • Each adjacent cell of actually reached cell is evaluated by value of f(n) and the one with lowest value of f(n) is chosen as the next one in sequence.
  • This advantage of modifying distance in A* gives wide range of modifications which can be applied in the algorithm in form of energy consumption and safety distance (Duchoň et al., 2014).

2.3. Assumptions

  • The complexity of USV path planning is massive and a number of simplifications have been recommended to reduce the intricacies of the problem (Azariadis and Aspragathos, 2005).
  • Here, the following assumptions have been made: 1. The map (study area) is considered to be in a confined sea environment near to Portsmouth harbor.
  • Kalman filter and other sensor measurements are used on a USV to determine the obstacle position over time.
  • Overlapping of elliptical shape with grid cell boundary is neglected.
  • The deliberative systems help in determining global waypoints while reactive systems are responsible for collision avoidance when dynamic obstacles come in the USV safety domain described in Fig.

2.4. Challenges of incorporating COLREGs in path planning algorithms

  • The COLREGs serves as a handbook for selecting avoidance manoeuvres.
  • Recently, several efforts have been made to integrate COLREGs in path planning algorithms for USVs (Svec et al., 2013, Kuwata et al., 2014).
  • These studies work safely in a scenario with very few complexities with an assumption that each vessel in the operational domain has the same amount of information about the current COLREGs situation and reacts in same way.
  • This hypothesis does not hold true in real time where each sailor interprets COLREGs based on speed, size and heading of the other vessel (Shah, 2016).
  • In addition to that, various external factors such as limited field of view, ocean currents and seamanship in case of breaching the COLREGs make it non-trivial to incorporate COLREGs rules into path planning framework used in complex scenarios.

2.5. Incorporating Guidance and Control System with Path Planning Algorithm

  • The general architecture of an USV operation in a maritime environment has basically three subsystems, namely, control and path planning, obstacle detection and avoidance (ODA) and communication and monitoring as shown in Fig.6.
  • The current study proposes an computationally effective and safer approach for generation of optimal waypoints for USV navigation in the desired environment.
  • Guidance is responsible to achieve motion control objectives in the physical environment in which the vehicle moves (Bibuli et al., 2009).
  • The easiest way is to use a classical autopilot system, so that commanded yaw angle generated from a line-of-sight (LOS) guidance algorithm can be controlled (assuming sufficient bandwidth) and cross track error is minimized.
  • In terms of autopilot and control system development, a detailed review of studies con- ducted on USVs has been discussed by Roberts (2008).

2.6. Collision avoidance in close encounter situation

  • The general architecture for a USV operation in a maritime environment described in Fig.6 shows that high level planners send waypoints to low level decision makers i.e. local control systems and obstacle avoidance subsystems to execute the waypoint following task.
  • When a time variant moving obstacle enters the working domain of the operating USV, it is expected that high level planners quickly regenerates new set of way points based on the current information of the environment.
  • Many other factors like relative velocity of the USV and the obstacle , the sensing horizon etc also plays an important role in such regeneration process.
  • In such transition, it is hereby required to have a quick response time from the high level planners which is one of the main objectives of the current study.
  • To enable the safe and secure operation of autonomous surface ships within the existing IMO requirement, a code of practice has been prepared by the UK Maritime Autonomous Systems Working Group and published by Maritime UK through the Society of Maritime Industries (UK, 2017).

3. Simulation Results

  • The proposed approach is simulated using C++ and OpenCV.
  • The simulations were repeated for 500 times, especially in terms of computational time, to account for variable computational power in OS Windows.
  • The average time from all repetitions was calculated for proper verification of the proposed approach.

3.1. Comparing A* approach with and without safety distance

  • The proposed study deals with inclusion of a safety distance criteria in the A* approach towards USV path planning.
  • In order to benchmark the safety distance approach and to decide upon an optimum value of safety distance, four arbitrary values, 10, 20, 30 and 40 pixels are taken as safety distance on a grid map (as shown in Fig. 2) and compared against an A* approach without safety distance in terms of computational time.
  • In terms of path length, simulations shows that the A* approach with and without safety distance constraint produces path of equal length i.e. 1.043 km although a difference in resultant path can be seen in Fig.11.
  • This leads to the fact that optimality remains conserved in path planning with decrease in computational effort in the proposed approach unlike ones adopted in literature towards path planning of USVs where an increase in computational cost has been observed with increase in path length for proposed approaches.
  • This value also provides enough time for local reactive techniques for collision avoidance in case where one or more moving obstacles are detected in the operational domain of the USV.

3.2. Constrained A* approach under static and partially dynamic environment

  • In order to understand the effectiveness of the proposed approach, simulations are conducted in binary maps of Portsmouth harbour comprising of static obstacles as well as moving obstacles.
  • The computational time again increases once the moving obstacle escapes out of the safety domain of the USV.
  • The results shown in Fig.18 shows path generated by proposed approach in different scenarios of an maritime environment with two moving obstacles.
  • In this case also the same pattern as found with the single moving obstacle scenario is observed.
  • The comparison of path length and computational time is shown in Fig.19 and Fig.20 respectively.

3.3. Constrained A* approach with environmental disturbances

  • Ocean currents generated in the upper layer of the ocean environment by atmospheric wind system are referred as sea surface currents (Fossen, 1995).
  • Real ocean currents are multi-directional and irregular, spatially and temporally.
  • Path length and computational time are compared for both scenarios shown in Fig.21 and results are presented in Fig.22 and Fig.23 respectively.
  • Along the same line, currents of 2.5 m/s are considered to understand the path planning pattern of USV under influence of strong ocean currents.
  • Fig.24 shows the path obtained by the proposed approach with currents moving in anti-clockwise and clockwise direction with intensity of 2.5 m/s.

3.4. Constrained A* approach with single moving obstacle and environmental disturbance

  • Since the complexity of the environment has increased, a more flexible safety distance constraint of 15 pixel has been adopted for this study in order to keep a proper trade off between optimal way points and environmental complexity.
  • Fig.28 shows the generated paths for different start time in the environment comprising of static obstacle, sea surface currents of 1.4 m/s moving in anticlockwise direction and moving obstacle (where each dynamic position is considered static).
  • Comparison of path length and computational time for all scenarios presented in Fig.28 are shown in Fig.27 and Fig.29 respectively.
  • In addition to that, most cases have been able to generate path within a reasonable computational time.
  • These results show that the proposed algorithm can generate safer way points for the USV voyage for long and short duration missions in a cluttered complex environment.

4. Conclusions

  • The objective of generating safer way points by keeping a safe distance from the obstacle was evaluated in simulations, conducted in various environments comprising of static obstacle, moving obstacle and sea surface currents of different intensities.
  • The upstream and effects of sea surface currents was evaluated and effect of sea surface currents with moving obstacle was also analysed.
  • The simulation results shows that the present approach generates safer way points for USV voyage in a computationally efficient manner against the conventional A* approach with no loss of optimality.
  • Another extension of the present work lies in considering heading angle constraint for USV, in such cases, where, path length is more important than computational time.
  • Most leading companies in USV operations are looking for the integration of COLREGs with optimal path planners to abide the working guidelines of the IMO.

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University of Plymouth
PEARL https://pearl.plymouth.ac.uk
Faculty of Science and Engineering School of Engineering, Computing and Mathematics
2018-12-01
A constrained A* approach towards
optimal path planning for an unmanned
surface vehicle in a maritime
environment containing dynamic
obstacles and ocean currents
Singh, Y
http://hdl.handle.net/10026.1/12372
10.1016/j.oceaneng.2018.09.016
Ocean Engineering
Elsevier
All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with
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The following is the author-generated post-print of an
article that appeared as
Y. Singh, S. Sharma, R. Sutton, D.[ C.] Hatton, and A.
Khan. A constrained A* approach towards optimal path
planning for an unmanned surface vehicle in a maritime
environment containing dynamic obstacles and ocean
currents. Ocean. Eng., 2018.
doi:10.1016/j.oceaneng.2018.09.016.
The article is copyright © 2018 Elsevier Ltd.. As permitted
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November 2019 onwards) under the Creative Commons CC
BY-NC-ND licence.

A constrained A* approach towards optimal path planning for an
unmanned surface vehicle in a maritime environment containing
dynamic obstacles and ocean currents
Yogang Singh
a,
, Sanjay Sharma
a
, Robert Sutton
a
, Daniel Hatton
a
, Asiya Khan
a
a
Autonomous Marine Systems (AMS) Research Group, University of Plymouth, Plymouth, PL4 8AA,
United Kingdom
Abstract
Efficient path planning is a critical issue for the navigation of modern unmanned surface ve-
hicles (USVs) characterized by a complex operating environment having dynamic obstacles
with a spatially variable ocean current. The current work explores an A* approach with an
USV enclosed by a circular boundary as a safety distance constraint on generation of opti-
mal waypoints to resolve the problem of motion planning for an USV moving in a maritime
environment. Unlike existing work on USV navigation using graph based methods, this
study extends the implementation of the proposed A* approach in an environment cluttered
with static and moving obstacles and different current intensities. The study also exam-
ines the effect of headwind and tailwind currents moving in clockwise and anti clockwise
direction respectively of different intensities on optimal waypoints in a partially dynamic
environment. The performance of the proposed approach is verified in simulations for dif-
ferent environmental conditions. The effectiveness of the proposed approach is measured
using two parameters, namely, path length and computational time as considered in other
research works. The results show that the proposed approach is effective for global path
planning of USVs.
Keywords: A star, Marine environment, Ocean currents, Path planning, Unmanned
surface vehicle
1. Introduction
Recent advances in electronic navigation and intelligent robots have become an impera-
tive aid to navigate marine vehicles effectively for applications ranging from reconnaissance
in hostile areas to operations in dangerous weather conditions (Loe, 2008). Although the
technology of unmanned surface vehicles(USVs) dates back to World War II, major research
Corresponding author at : School of Engineering, University of Plymouth, PL4 8AA, Plymouth, United
Kingdom
Email address: yogang.singh@plymouth.ac.uk (Yogang Singh)
Preprint submitted to Ocean Engineering September 2, 2018

towards development of USVs technology and improving their autonomy started after the
successful implementation of USVs in the 1990-1991 Gulf war (Campbell and Naeem, 2012).
Path planning is an important layer in the mission management system of an USV
voyage. In accordance with the current level of autonomy, USVs needs an effective and
safe path planning approach in a cluttered operating environment. A substantial amount
of research has been conducted in the area of path planning of unmanned surface vehicles.
Path planning for USVs can be classified into two categories, namely, reactive approaches
(Khatib, 1986, Borenstein and Koren, 1991, Mohanty and Parhi, 2013, Fiorini and Shiller,
1998) where vehicles makes decision en route and deliberative approaches where vehicles
follows a predetermined path (Hart et al., 1968, Holland, 1992, Kennedy, 2011). Several
computational approaches comprising of evolutionary methods such as Genetic Algorithm
(GAs) or Particle Swarm Optimisation (PSO) (Zeng et al., 2015, Aghababa, 2012), graph
search techniques (Garau et al., 2005, Singh et al., 2017a), artificial potential field (APF)
(Warren, 1990, Singh et al., 2017b) and fast marching (FM) (Liu et al., 2017, Petres et al.,
2007) have been applied in path planning of marine vehicles.
Ocean environmental effects and moving obstacles play the most significant role in path
planning of USVs and very few literatures have covered their effect on path planning in
the last decade (Tam et al., 2009, Statheros et al., 2008).Neglecting environmental effects
in path planning not only leads to significant wastage of energy in USVs while navigating
in strong currents but could also elevate the potential danger of impact with the obstacles
(moving or static) in an ocean environment. In order to save energy, avoid the collision
and to increase the endurance of USVs enabled with limited computational resources, it is
important to plan the USVs voyage in advance before the mission commences by considering
environmental effects and dynamic obstacles in path planning of USVs. Traditionally, grid
search techniques have been found most efficient in generating path in fastest computation
time compared to other reactive approaches adopted in path planning of robots (Mohanty
and Parhi, 2013).
The paper is organized as follows : In the current section, the literature pertaining to
path planning of USV has been described with major contributions of the current study
being explained. In section 2, a detailed overview of the methodology adopted in the cur-
rent study is presented. In the subsequent section, simulation studies conducted in various
environmental scenarios are reported and the proposed approach is benchmarked. The con-
clusions of the current study are reported in the final section.
1.1. Related work
Many studies have been conducted on the subject of grid based path planning in the
area of marine vehicles from different perspectives of collision avoidance, heading constraint,
environmental disturbances and energy consumption. By reviewing the literature on the
subject of optimal path planning in marine vehicles, most of these studies have been in
the area of autonomous underwater vehicles (AUVs) (Alvarez et al., 2004, Garau et al.,
2005, Kruger et al., 2007, Zeng et al., 2016, Soulignac, 2011, Kumar et al., 2005)and very
fewer studies have been in the area of path planning of USVs. AUVs cannot operate in
all environmental conditions due to limited speed and onboard capabilities against USVs
2

which are more suited for operation in areas of high military, shipping or fishing activity,
due to acoustic interference, collision risk, and net entanglement. AUVs are also less well
suited to tidally dominated shallow-water settings that have high levels of anthropogenic
infrastructure and activity. This leads to requirement of development of dedicated path
planning approaches for USVs against path planning approaches adopted for AUVs.
The grid based path planning was first proposed in form of the Dijkstra algorithm (Di-
jkstra, 1959) which was later extended to the A* algorithm by introducing an heuristic cost
(Hart et al., 1972) to speed up the search process by pruning the search space. Generally in
grid based path planning, the objective is to find the shortest path by avoiding static obsta-
cles (Stentz, 1994). This approach was first introduced into USV path planning, where an
improved three layered architecture towards USV path planning in a harbour was proposed
by combining a reactive and A* approach (Casalino et al., 2009). In another work, a A*
approach was extended by combining a grid based path planner with a locally bounded op-
timal planner towards USV path planning in uncertain sea environment (Svec et al., 2011).
The International Maritime Organization (IMO) ((IMO, 1988, 1995, 2007)) has suggested
certain regulations for navigation in a marine environment for collision avoidance commonly
known as International Regulations for Preventing Collisions at Sea(COLREGs). A COL-
REGs based A* approach was proposed for way point navigation of an USV complying with
Rule 14 of COLREGs in an environment cluttered with static and moving obstacles (Naeem
et al., 2012). A modified A* approach, Finite Angle A*(FAA*) towards obtaining shorter
path length than classic A* approach has been adopted in a study conducted on USV path
planning in an environment comprising static obstacles with a constraint of keeping safe
distance from obstacles (Yang et al., 2012).
Currently a substantial amount of research in mobile robotics towards modifying the
conventional A* algorithm to improve its performance as per the mission and kinematic
requirement of the robot i.e. A* with Post Smoothing (Rabin, 2000), Field D* (David and
Anthony, 2005), Theta* (Nash et al., 2007) and D* Lite (Koenig and Likhachev, 2002)
has been conducted. Owing to technical similarities between mobile robots and USVs,
some of the improved approaches have been extended in path planning of USVs. USVs
are generally constrained by yaw rate and heading angle in real time manoeuvring. A
modified A* algorithm, Theta*, for search in 3D Euclidean space at all orientation was
implemented for USV path planning complying with heading angle of USV and compared
with conventional grid based 3D path planners (Kim et al., 2012). In a further work, the
Theta* algorithm was improved in terms of computational time and path length against
conventional 3D path planners for USV path planning in form of ARC-Theta* algorithm
(Kim et al., 2014), which considers angular rate (yaw rate) of USV in path planning. Another
improvement in the A* algorithm for USV path planning was proposed by a modifying
heuristic for ocean environment with surface currents constrained to heading angle and
diverse water depth (Lee et al., 2015). Another novel work in area of optimal path planning
of USVs has been conducted recently by using FM
2
algorithm, an optimal approach to FM
method by considering environmental disturbances (Garrido and Moreno, 2016).
3

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Frequently Asked Questions (2)
Q1. What are the contributions in "A constrained a* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents" ?

Unlike existing work on USV navigation using graph based methods, this study extends the implementation of the proposed A * approach in an environment cluttered with static and moving obstacles and different current intensities. The study also examines the effect of headwind and tailwind currents moving in clockwise and anti clockwise direction respectively of different intensities on optimal waypoints in a partially dynamic environment. 

The approach is found to be robust, computationally efficient and can be extended for real time path planning of USVs in confined water. In future work, it is planned to extend the work in development of a path follower approach working in conjugation with proposed approach for a reactive path planning in scenarios involving close encounters. A challenging 28 extension of the current work lies in fact of finding a heuristic cost function which can take into account rules of the COLREGs without compromising the optimality and computational effort required to find a feasible trajectory.