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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
TL;DR: Even after embargo period expires, authors' right to distribute as green open access is conditional on the green openAccess version including a DOI link, and on thegreen open access version being distributed under the Creative Commons CC-BY-NC-ND licence.
About: This article is published in Ocean Engineering.The article was published on 2018-12-01 and is currently open access. It has received 169 citations till now.

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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Citations
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01 Jan 2007
TL;DR: The possibilities for a novel type of AUV mission deployment in fast flowing tidal river regions which experience bi-directional current flow are examined, enabling extended monitoring of otherwise energy-exhausting, fast flow environments.
Abstract: This paper addressesthe problemsof automatically planning AutonomousUnderwater Vehicle (AUV)pathswhichbestexploit complex current data, from computational estuarine modelforecasts, whilealsoavoiding obstacles. Inparticular weexamine thepossibilities foranovel typeofAUV mission deployment infastflowing tidal river regions whichexperience bi-directional current flow. These environments are interesting in that,by choosing an appropriate pathinspaceandtime, anAUV maybothbypass adverse currents whicharetoofasttobeovercome bythe vehicle's motorsandalsoexploit favorable currents toachieve fargreater speeds thanthemotorscouldotherwise provide, while substantially saving energy. TheAUV can"ride" currents bothupanddowntheriver, enabling extended monitoring of otherwise energy-exhausting, fastflowenvironments. The paperdiscusses suitable pathparameterizations, costfunctions andoptimization techniques whichenable optimal AUV paths to be efficiently generated. Thesepathstakemaximum advantage oftheriver currents inordertominimize energy expenditure, journey timeandothercostparameters. The resulting pathplannercan automatically suggest useful alternative mission start andendtimes andlocations tothose specified bytheuser. Examples arepresented fornavigation in a simple simulation ofthefastflowing HudsonRiverwaters around Manhattan.

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TL;DR: From the results of both scenarios for overtaking and following, it illustrates that the timing is significant for strategy selection and should well consider the complex situation and ship behaviours, moreover, the proposed approach can be used for intelligent strategy selection.

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TL;DR: This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments.
Abstract: Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

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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.