A hierarchal planning framework for AUV mission management in a spatiotemporal varying ocean

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Abstract

This paper provides a hierarchical dynamic mission planning framework for an AUV to accomplish task-assign process in a restricted time operating in uncertain undersea environment. A high-level reactive mission planner is developed for task priority assignment, guiding the vehicle toward a target of interest, and managing on-time mission completion. A low-level motion planner is also developed to handle unexpected changes of the dynamic terrain by re-generating optimal trajectories. The mission planner reactively re-arranges the tasks based on mission/terrain updates. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The Biogeography-Based Optimization (BBO) algorithm is used as the computational engine of the framework in both mission and motion planners. The simulations results indicate the significant potential of the proposed hierarchical framework in providing efficient solutions for mission success and its applicability for real-time implementation.

Introduction

Autonomous operation of Autonomous Underwater vehicles (AUVs) in a vast, unfamiliar and dynamic underwater environment is a complicated process, especially when the AUV is obligated to react to environment changes, where usually a-priory information is not available. Recent advancements in sensor technology and embedded computer systems has opened new possibilities in underwater path planning and made AUVs more capable for handling complicated long-range missions. However, there still exist major challenges for this class of the vehicle, where the surrounding environment has a complex spatiotemporal variability and uncertainty. Ocean current variability affect vehicle's motion, for example it can perturb its safety by pushing that to an undesired direction. Consequently, this variability can also have a profound impact on vehicle's battery usage and its mission duration. The robustness of a vehicle's path planning to this strong environment variability is a key element to its safety and mission performance. Thus, robustness of the trajectory planning to current variability and terrain uncertainties is essential to mission success and AUV safe deployment.

On the other hand, an AUV should carry out complex tasks in a limited time interval. However, existing AUVs have limited battery capacity and restricted endurance, so they should be capable of managing mission time. Obviously, a single AUV is not able to meet all specified tasks in a single mission with limited time and energy, so the vehicle has to effectively manage its resources to perform effective persistent deployment in longer missions without human interaction. In this respect, time management is a fundamental requirement toward mission success that tightly depends on the optimality of the selected tasks between start and destination point in a graph-like operation terrain. Hence, design of an efficient mission planning framework considering vehicle's availabilities and capabilities is essential requirement for maximizing mission productivity. Many efforts have been devoted in recent years for enhancing an AUV's capability in robust motion planning and efficient task assignment. Although some improvement have been achieved in other autonomous systems; there are still many challenges to achieve a satisfactory level of intelligence and robustness for AUV in this regard.

AUVs capabilities in handling mission objectives are directly influenced by routing and task arrangement system performance. The main issue that should be covered by route planning system is to direct vehicle(s) to its destination in a network while providing efficient maneuver and optimizing travel time. An integrated mission task assignment and routing strategy is proposed in [1] to serve the AUVs routing problem in order to deliver customized sensor packages to mission targets at scattered positions, while minimising total energy cost in the presence of ocean currents. The AUV routing problem is investigated with a Double Traveling Salesman Problem with Multiple Stacks (DTSPMS) for a single-vehicle pickup-and-delivery problem by minimizing the total routing cost [2]. A large scale route planning and task assignment joint problem related to the AUV activity has been investigated in [3] by transforming the problem space into a NP-hard graph context and using the heuristic search nature of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to find the best waypoints and their corresponding tasks. Later on, the same concept is extended by MahmoudZadeh et al., to AUV routing in a semi-dynamic network, while performance of the BBO and PSO algorithm is tested on single vehicle's routing approach [4]. Growth of the graph complexity, or in general, enlargement of the search space increases the computational burden that is often a problematic issue with deterministic methods such as mixed integer linear programming (MILP) proposed by Yilmaz et al., for governing multiple AUVs [5]. In terms of task assignment, many typical AUV missions are limited to executing a list of pre-programmed instructions and completing a predefined sequences of tasks. The majority of the mentioned researches particularly focuses on task and target assignment and time scheduling problems without considering requirements for vehicle's safe deployment or quality of its motion in presence of environmental disturbances. A vehicle's safe and confident deployment is a critical issue that should be taken into consideration at all stages of a mission in a vast and uncertain environment. Some of the existing AUV trajectory/path planning approaches are discussed as follows, which are more concentrated on vehicles deployment encountering dynamicity of the operating environment.

Various strategies have been developed and applied to the AUV path-planning problem in recent years. The well-known direct method of optimal control theory, called inverse dynamics in the virtual domain (IDVD) method, was employed to develop and test a real-time trajectory generator for realization on board of an AUV [6], [7]. A sliding wave front expansion algorithm applying continuous optimization techniques has been presented by Soulignac for AUVs path planning in presence of strong current fields [8]. Jan et al., investigated higher geometry maze routing algorithm for optimal path planning and navigating a mobile rectangular robot among obstacles [9]. Nevertheless, this strategy may not be appropriate for AUV dynamic environments where the current field changes continuously during the mission. Earlier proposed methods [8], [9] are capable of providing optimum path planning for AUV using previous information for re-planning process, which is computationally reasonable in generating accurate local trajectories; however, they modelled the environment as a 2D space, which is inefficient for application of the AUVs, as a 2D representation of a marine environment doesn't sufficiently embody all the information of a 3D ocean environment and the vehicle's six degree of freedom. The evolution-based strategies like Differential Evolution (DE) [10], GA [11] and PSO [12] are another approach that has been applied successfully to the path planning problem and are fast enough to satisfy time restrictions of the real-time applications. A real-time online evolution based path planner was developed for AUV rendezvous path planning in a cluttered variable environment, in which the performance of four evolutionary algorithms of Firefly Algorithm (FA), BBO, DE, and PSO is tested and compared in different scenarios [13]. A Quantum-based PSO (QPSO) was applied by Fu et al. [14], for unmanned aerial vehicle's path planning, in which only the off-line path planning in a static known environment was implemented. However, the off-line planning cannot sufficiently cover the dynamicity and uncertainty of underwater environment. Although various path planning techniques have been suggested for autonomous vehicles, AUV-oriented applications still have several difficulties when operating across a large-scale geographical area. The recent investigations on path planning that incorporate variability of the environment have assumed that planning is carried out with perfect knowledge of probable future changes of the environment [15], [16], while in the reality, accurate prediction of the environmental events (such as currents or obstacles state variations) is impractical specially in longer operations. Even though available ocean predictive approaches operate reasonably well in small scales and over short time periods, they produce insufficient accuracy to current prediction over long time periods in larger scales, specifically in cases with lower information resolution [17]. Moreover, current variations over time can affect moving obstacles (or waypoints in some cases) and drift them across a vehicle's trajectory; therefore, the planned trajectory may change and become invalid or inefficient. Proper estimation of the events in such a dynamic uncertain terrain in long range operations, outside the vehicle's sensor coverage, is impractical and unreliable. This becomes even more challenging for re-planning process, when a large data load of variation of whole terrain condition should be computed repeatedly. Hence, using such an estimation methods is computationally inefficient and unnecessary as only awareness of environmental changes in vicinity of the vehicle is sufficient for performing a prompt reaction. As mentioned earlier, the path planning problem principally deals with the quality of a vehicle's motion between two points. On the other hand, vehicles routing strategies are not flexible like path planning methods in terms of handling environment sudden changes, but they give a general overview of the area that an AUV should fly through (general route), which means reducing the operation area to smaller operating zone for vehicle's deployment.

Considering the mentioned challenges and to produce a reliable mission plan for a large scale time-varying underwater environments, this paper proposes a reactive hybrid framework that comprises an efficient mission planning system combined with real-time path planning. This framework improves a vehicle's ability to complete as much of its mission as possible within the time available, while a path planner is designed to operate in a lower level and concurrently plan trajectory between waypoints included in the task sequence. The path planner acts as an inner function of the mission planner and should be fast enough to handle unforeseen changes and regenerates an alternative trajectory that safely guides the vehicle through the specified waypoints with minimum time/energy cost. Both of the planners operate individually and concurrently while sharing their information, so a constant interaction exists between them. This paper is a continuation of previous research [18] in which the environment modeled to be more realistic comprising uncertainty of moving/afloat objects and dynamic multiple-layered time varying ocean current; accordingly, the path planner in current study is facilitated with dynamic re-planning capability, which have not been addressed previously [18]. The reactive hybrid model decides whether to carry out the path re-planning or mission re-planning procedure according to the raised situation. The path re-planning is performed to cope with dynamic changes of the operating environment over time. On the other hand, mission re-planning is performed to manage the lost time in cases that the path planner process takes longer than expectation. The proposed re-planning procedure in both layers improve the robustness and reactive ability of the AUV to the environmental changes and enhance its performance in accurate mission timing.

In the core of the proposed strategy, both mission planner and local path planners make use of the Biogeography-based Optimization (BBO) algorithm. The argument for application of BBO in solving Non-deterministic Polynomial-time (NP) hard problems is strong enough due to its remarkable competency in scaling with multi-objective and complex problems. In this algorithm solutions of one generation are transferred to the next and never discarded but modified. This characteristic of the BBO enhances its exploitation ability. Solving NP-hard problems is computationally challenging and currently there is no polynomial time algorithm to handle a NP-hard problem of even moderate size. Furthermore, finding a pure optimum solution is only possible when the environment is fully known and no uncertainty exists. The modeled underwater environment in this paper corresponds to a highly dynamic uncertain environment. The BBO is one of the fastest meta–heuristics algorithms introduced for solving NP-hard complex problems. Although the captured solutions do not necessarily correspond to a pure optimal solution, controlling the computational time is more preferable in this research due to the real-time application of the AUV operations; hence the BBO is employed to find feasible and near optimal solutions in competitive CPU time. The proposed reactive autonomous/reactive system reduces run time by splitting the operation terrain to smaller zones for the path planner. To evaluate the performance of this framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The paper is organized as follows. The mathematical representation of the underwater terrain is provided by Section 2. In Section 3, the application of the BBO algorithm on mission planning and local path planning is explained. This system is implemented in MATLAB®2016 and its performance is analyzed by Section 4. Ultimately, Section 5 concludes the paper.

Section snippets

Mathematical representation of the underwater terrain

Existence of prior information about the terrain (e.g. location of coasts; static obstacles as the forbidden zones for deployment; position of the starting and ending points) improves AUV's capability in robust path planning. To model a realistic marine environment, a three dimensional terrain in scale of {10 × 10 km (x-y), 1000 m(z)} is considered based on realistic example map presented by Fig. 1, in which the operating field is covered by uncertain static-moving objects, several fixed

Overview of the BBO algorithm

The BBO is an evolutionary algorithm inspired by equilibrium theory of island biogeography concept [22] that uses the idea of emigration, immigration, and total number of species in an island. The initial population of candidate solutions are coded with geographically isolated islands known as habitats. Each habitat (solution) has a quantitative performance index corresponding to its fitness and called Habitat Suitability Index (HSI). Respectively, the high quality habitats have higher HSI and

Discussion on simulation results

To make the simulation process more realistic different uncertain static/dynamic obstacles are taken into account and a real map data is utilized, where the water covered area is classified by k-means method. Beside the uncertainty of operating field, water current can also affects mission objectives and sometimes makes the mission impossible. These issues, therefore, need to be address thoroughly by the designer in accordance with the type of the mission. Status of the operating field gets

Conclusion

A two-level dynamic mission-motion planning for AUV's optimal task-assignment and time management has been developed. The higher level mission planner provides the best sequence of waypoints (tasks) with respect to the mission objectives. By doing this, the optimal deployment zone is selected and the search area is minimized. Afterward, the local reactive path planner starts generating optimal collision-free path through the listed waypoints; copping any unexpected environmental changes is done

Somaiyeh MahmoudZadeh received her PhD from Flinders University of South Australia in 2017 in Computer Science (Robotics and Autonomous Systems). She is currently acting as Postdoctoral Research Fellow in Faculty of IT, Monash University. Her area of research includes computational intelligence, autonomy and decision making, mission planning, situational awareness, and motion planning of autonomous underwater vehicles.

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  • Cited by (0)

    Somaiyeh MahmoudZadeh received her PhD from Flinders University of South Australia in 2017 in Computer Science (Robotics and Autonomous Systems). She is currently acting as Postdoctoral Research Fellow in Faculty of IT, Monash University. Her area of research includes computational intelligence, autonomy and decision making, mission planning, situational awareness, and motion planning of autonomous underwater vehicles.

    David MW Powers is Professor of Computer Science and Director of the Centre for Knowledge and Interaction Technology and has research interests in the area of Artificial Intelligence and Cognitive Science. His specific research framework is Cognitive Science perspective on AI and its practical applications. He is known as a pioneer in the area of Parallel Logic Programming, Unsupervised Learning.

    Karl Sammut is Professor with the Engineering Discipline in the School of Computer Science, Engineering and Mathematics. His areas of expertise are in embedded systems, robotics, smart structures, and maritime electronics. His particular areas of research specialization are concerned with Autonomous Underwater Vehicles (AUVs) including, navigation, optimal guidance and control systems, mission planning systems, and propulsion systems for AUVs.

    Adham Atyabi received his PhD from Flinders University of South Australia in 2013. He is currently acting as Technology Lead in Seattle Children's Innovation & Technology Lab and Senior Postdoctoral Fellow at University of Washington. His research interests include Brain Computer Interfacing, EEG analysis, Eye Tracking & Image Processing, Signal Processing, Machine Learning, Swarm and Cognitive Robotics, and Knowledge Transfer.

    Amir Mehdi Yazdani received his PhD from Flinders University of South Australia in 2017. He is currently postdoctoral research fellow in the Centre of Maritime Engineering, Control and Imaging at Flinders University. His main research interests focus on guidance of unmanned vehicles, optimal control and state estimation theory, intelligent control applications.

    Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. A. Chaudhary.

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