Autonomous Planning and Mission Management for Future Military AUVs (original) (raw)
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AUTONOMOUS AUV MISSION PLANNING AND REPLANNING - TOWARDS TRUE AUTONOMY
This paper discusses concepts developed under the SEA led UK MoD Battlespace Access Unmanned Underwater Vehicle (BAUUV) programme to provide higher levels of military AUV autonomy. BAUUV aims to identify and assess the technology readiness relating to a range of future (2010-2015) UK military missions and to perform focussed technology development activities to address key technology gaps. One key gap relates to provision of suitable levels of autonomy to allow a UUV to perform long duration military missions. Contemporary autonomous underwater vehicles generally execute a prescripted mission plan with simple branching. However, future military missions will require higher levels of autonomy such that the vehicle can operate with a minimum of supervision and adapt to changing military goals, onboard health and situational awareness. In order to reduce onerous and often impracticable human supervision and communications requirements, future military vehicles will need to perform some level of autonomous mission replanning and decision making in order to adapt to changes in AUV situational awareness, changes in knowledge of vehicle status and energy availability and changes in military goals. Autonomous mission replannning algorithms that aim to provide this functionality have been developed and evaluated. The resulting mission replanning software utilises a hierarchical iterative approach with initial rough planning based on goal selection and sequencing activities being followed by detailed task planning and plan tuning. Replans can be instigated by user defined changes in goal characteristics or priorities or by internal triggers such as an unexpected change in energy usage or task progress. In addition to core mission replanning algorithms, software relating to specific task planning/replanning modules is being considered. For example, an autonomous transit task planner has been developed. This is capable of autonomously defining and costing a transit based on encyclopaedic knowledge of subsurface currents, detection and physical risks. The AUV “personality” is defined by the relative vehicle energy, risk and time priorities which drive the selection of a particular transit plan. Other task planners being considered include those relating to communications, survey, reconnaissance, REA and logistics goals. Within a typical three-layer UUV hierarchical control architecture, the onboard mission and task replanning elements would form part of the top level deliberative elements and would typically interface to a sequencing layer via an updatable mission script. The sequencing layer would interface to task achieving behaviours and low level autopilot modes, potentially via a collision and obstacle avoidance module. In addition to onboard elements, this paper discusses associated concepts relating to intuitive user interfaces and planning aids. Goal based planning/replanning technology enables the user to specify a mission based on a series of military goals, constraints and priorities rather than having to define a detailed mission script. This should increase the speed for the definition, validation and modification of future missions and reduce the skill requirement for a future military UUV user. An example goal based user interface prototype is presented. Finally, after describing current study results and status, the paper will touch upon ongoing trial activities to advance the technology from TRL 4/5 to TRL 6.
A Human-on-the-Loop Autonomy Architecture for Resident-AUV Undersea Support Infrastructure
2020
The use of Resident Autonomous Underwater Vehicles (R-AUVs) is a necessary step towards increasing the safety and reliability of undersea infrastructure ranging from communication cables to oil pipelines and undersea observatories. Undersea Support Infrastructure (USI) for R-AUVs will provide docking, energy and communication services. Furthermore, it will be able to autonomously interact with R-AUVs, while enabling remote human operators to oversee, and in some cases direct, the R-AUVs and USI operations. This is particularly critical in situations where assured communications between operators and the USI are not guaranteed. This paper proposes an autonomy architecture for USIs that pursues a vertical and horizontal separationof-concerns architecture-design approach and builds on welldocumented autonomy and autonomic system design principles. Horizontal separation allows for configuration strategies and behavior policies to be defined, selected, executed and monitored by loosely c...
Adaptive AUV mission management in under-informed situations
OCEANS 2010 MTS/IEEE SEATTLE, 2010
Autonomous Underwater Vehicles (AUVs) are in high demand within the offshore industry and maritime research, mainly used for bathymetry and data acquisition. The control architectures of these AUVs mimic this primary function by focusing on strict mission plans as these kind of application require, thus reducing the need for direct sensor reaction to emergency situations. The emerging needs for more complex underwater application like the inspection of structures, search missions or taking samples from the floor or in the water column with respect to certain environmental conditions demand more adaptive, currently not existing, control architectures. The main problem hereby is that, opposed to non-underwater application scenarios for autonomous systems, the lack of a stable communication channel to the vehicle demands complete autonomy. The architecture proposed in this paper aims at tackling the issue of unpredictability. The main issue, especially in exploration or inspection missions, is that little is known at the beginning of the mission. This lack of information makes planning meaningless, as the planner has no idea whatsoever as to what should be done while on site. Our proposed architecture offers to replace, in these under-informed situations, planning-based approaches by a plan management approach. This approach is able to use both predictive (planning) approaches and behaviours (reactive) approaches to control the system, which is then used to execute and control execution of functional components. The mixing of these decision-making schemes being done based on the information available to the system. This paper presents the general idea of our architecture as well as the implementation and a validation experiment with the AUV AVALON.
Automatic interface for AUV mission planning and supervision
2010
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.
2016
Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an unknown underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large scale environment. The LPP module at the lower level deals with safety consid...
Autonomous underwater vehicles for scientific and naval operations
2006
Recognizing the potential of autonomous underwater vehicles for scientific and military applications, in 1997 MIT and the NATO Undersea Research Centre initiated a Joint Research Project (GOATS), for the development of environmentally adaptive robotic technology applicable to mine counter measures (MCM) and rapid environmental assessment (REA) in coastal environments. The August 2001 GOATS Conference marked the end of this 5 years project, but did not mark the end of the work. The Centre initiated in 2002 a new long-term programme to explore and demonstrate the operational benefits and performances of AUV for covert preparation of the battlespace. Recently the work addressed the evaluation of commercial off-the-shelf (COTS) AUV technology for MCM operations in response to terrorist mining of port. The paper summarizes the work performed and refers to the scientific publications derived from the AUV programme at the NATO Undersea Research Centre. #
3D Visualization of Mission Planning and Control for the NPS Autonomous Underwater Vehicle
1990
Unmanned vehicles can operate where humans cannot or do not want to go. The last decade's advances in computer processor capability and speed, component miniaturization, signal processing, and high-energy density power supplies have made remotely operated vehicles (ROV's) and, to some extent, autonomous vehicles, a reality. In an effort to further advance this technology, the Naval Postgraduate School (NPS) is constructing a small autonomous underwater vehicle (AUV) with an onboard mission control computer. The mission controller software for this vehicle is a knowledge-based artificial intelligence (Al) system requiring thorough analysis and testing before the AUV is operational. We discuss how rapid prototyping of this software has been demonstrated by developing controller code on a LISP machine and using an Ethernet link with a graphics workstation to simulate the controller's environment. Additionally, we discuss the development of a new testing simulator using a KEE expert system shell that is designed to examine AUV controller subsystems and vehicle models before integrating them with the full AUV for its test environment missions. This AUV simulator utilizes an interactive Mission Planning Control Console and is fully autonomous once initial parameters are selected.
Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission
arXiv: Robotics, 2016
An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of autonomy for any particular underwater mission to fulfil the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating filed. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method is established upon the heuristic search potency of the Particle Swarm Optimization (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NP-hard path planning problem. The proposed hybrid method, is highly efficient for having a reactive guidance framework that guarantees successful completion of missions specifically in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions. Keywords-Autonomous underwater vehicles, Path planning, Route planning, Autonomy, Evolutionary optimization 1. Introduction Autonomous Underwater Vehicles (AUVs) have been discovered as the most cost-effective and expedient technology over the past and coming years. Recent breakthroughs in computer systems and sensor technologies have greatly expanded the range of missions to be performed by AUVs in the past years. This widening operating range, however, is intertwined with the appearance of new complexities, for instance propagation of uncertainty and accumulation of data, crystalizing the underwater mission into a challenging scenario. Increasing the mission productivity and ensuring the vehicles safety are two main concepts that should be satisfied in all stages of the mission considering dynamic changes of environment. Hence, vehicle needs a certain degree of autonomy to perform efficacious and safe operation in such a severe environment. An efficient motion planning strategy is a requisite to reach a satisfactory level of autonomy toward accomplishing underwater mission objectives. Efficient routing strategy promotes vehicles capability in task priority assignment, mission time management, and increasing mission productivity, while real-time local path planner is capable of satisfying safe and optimal deployment in uncertain, dynamic, and cluttered ocean environments. Respectively, the previous attempts in this scope can be divided into two main categories of vehicles optimum path planning along with the vehicle routing and task scheduling approaches.
2016
Today AUVs operation still remains restricted to very particular tasks with low real autonomy due to battery restrictions. Efficient motion planning and mission scheduling are principle requirement toward advance autonomy and facilitate the vehicle to handle long-range operations. A single vehicle cannot carry out all tasks in a large scale terrain; hence, it needs a certain degree of autonomy in performing robust decision making and awareness of the mission/environment to trade-off between tasks to be completed, managing the available time, and ensuring safe deployment at all stages of the mission. In this respect, this research introduces a modular control architecture including higher/lower level planners, in which the higher level module is responsible for increasing mission productivity by assigning prioritized tasks while guiding the vehicle toward its final destination in a terrain covered by several waypoints; and the lower level is responsible for vehicle's safe deploym...