Collaborative autonomy for naval stand-off mine countermeasure - - PDF document

UDT 2020 Collaborative autonomy for stand-off MCM operations Unmanned, Remotely Piloted & Autonomous Systems

Collaborative autonomy for naval stand-off mine countermeasure operations

• R. van Vossen1, A.L.D. Beckers2 and J.J.M. van de Sande3

1Senior Scientist, TNO, The Hague, The Netherlands, robbert.vanvossen@tno.nl 2Senior Consultant, TNO, The Hague, The Netherlands 3Scientist, TNO, The Hague, The Netherlands

Abstract — To increase the efficiency of stand-off mine countermeasure (MCM) operations, multiple heterogeneous systems are to be used in parallel. The implementation of an efficient stand-off MCM concept therefore requires that systems are able to cooperate, together with a high level of autonomy to aid the task execution

• f individual systems. The technology for adaptive search in a complex terrain and autonomous contact reacquisition

by multiple systems is developed and demonstrated in this paper, covering self-awareness for sensor and communications management.

1 Introduction

Many Navies are moving towards implementing stand-off mine countermeasures (MCM) concepts in which heterogeneous collaborative unmanned systems are used for conducting the detect-to-engage tasks. Potential benefits are that (i) new sensor and system concepts can be introduced to improve the effectiveness of MCM

• perations, and (ii) there is an opportunity to increase the

efficiency of MCM operations by using a large number of unmanned systems in parallel. To increase the efficiency of MCM operations, scalable solutions are sought, in which multiple systems can be deployed in parallel. In such a scalable stand-off MCM concept, individual systems are tailored to conduct specific MCM tasks, and cooperation between systems is needed to complete the entire detect-to engage chain. The realization of a successful stand-off MCM concept with a group of collaborating systems requires that: (i) stand-off MCM systems are able to exchange relevant information; (ii) stand-off MCM systems can be tasked to conduct the detect-to-engage tasks as a team; (iii) stand-off MCM systems are able to adapt themselves to deal with uncertain/unknown environmental conditions and system performance limitations. This paper focuses on collaborative autonomy to aid the execution of mine-hunting tasks, addressing challenges related to limitations in communication, navigation accuracy, and variations in sensor performance. In an accompanying paper, Van Velsen et al. [1] present a framework for collaborative and adaptive mission management for naval stand-off mine hunting operations. Mission management involves the planning and evaluation for the different stand-off MCM systems.

2 Technical approach

In order to be able to investigate collaborative autonomy a testbed has been realised, comprising:

• Multiple autonomous underwater vehicles (AUVs)

with different sensor payloads, including side scan sonar (SSS), multi-beam echo sounder (MBES),

• ptical camera, a software-defined underwater

communications modem, and aided inertial navigation system;

• Relay nodes for underwater communications.

The individual systems have an open architecture, enabling the implementation of an autonomy framework to develop not only functionality to autonomously or adaptively execute MCM tasks, but also to pass relevant information to the coordinating mechanism and to other systems in the collaboration. Coordination information typically consists of command and control information: task initiation, task performance and task completion. Collaboration information is about mission data to enable

• ther systems to execute their tasks such as mine like

echo’s, contacts and contextual information.

3 Results

Within this framework functionality has been developed for adaptive mine search and for adaptive multi-vehicle contact reacquisition. 3.1 Adaptive mine search An adaptive mine search survey is presented in Figure 1. The area has a complex bathymetry with sand dunes. As a consequence, it is difficult to realize full coverage with a SSS that operates at small grazing angles. In Figure 2 a SSS image of the concerning area is depicted in which sand dunes block the view of the sonar. The mission example shows that SSS data is interpreted in-mission to

• btain coverage information, and subsequently plan

additional passes using a different orientation. Because full coverage is not realized using two different

• rientations, the vehicle decides to use a different sensor,

UDT 2020 Collaborative autonomy for stand-off MCM operations Unmanned, Remotely Piloted & Autonomous Systems an MBES, that operates at steeper grazing angles to fill the remaining gaps in the coverage.

• Fig. 1. Adaptive mine search in an area with sand dunes. In the first pass, shadows of sand dunes result in gaps in the coverage (top

left). The gaps are automatically detected and an adaptive new plan is scheduled along a different orientation (top right). Having completed the second pass, there still remain gaps in the coverage (bottom left). A final survey is scheduled to fill the gaps using an MBES rather than a SSS (bottom right).

• Fig. 2. Side scan sonar image recorded in the mission area. On the starboard side of the AUV sound of the sonar is blocked by a sand

dune, leaving a shadow in the image. The area where the shadow resides is considered as not covered.

3.2 Adaptive multi-vehicle contact reacquisition In stand-off naval MCM operations, contact reacquisition is a challenging task due to inherent navigation and localization uncertainties. Furthermore, sensors that are commonly used for the identification, optical cameras, usually have a limited swath width, especially in limited underwater visibility conditions. As a consequence, contact reacquisition can become time consuming, especially when many contacts are to be reacquired. In mission planning, decisions have to be made on the tasking of individual reacquisition and identification systems in comms-limited environments [1]. When the systems are tasked, each system has to optimally plan and execute its reacquisition and identification task. It has been shown and demonstrated in [2] that the use of an auxiliary sensor to aid the navigation solution can greatly improve the efficiency of contact reacquisition and optical identification. To reduce the reacquisition time of a vehicle, landmarks are used to improve the navigation solution. During the search phase landmarks are detected by the

UDT 2020 Collaborative autonomy for stand-off MCM operations Unmanned, Remotely Piloted & Autonomous Systems search vehicle and acoustically transmitted to the identification vehicle. The identification vehicle in this way builds up a map of landmarks in the area in which it will need to do reacquisition and identification. Figure 3 shows an example side scan image with the detected landmarks indicated by the green boxes.

• Fig. 3. Side scan sonar image recorded in the mission area. Detected landmarks are indicated by the green boxes. The landmarks are

broadcast acoustically to other vehicles to use as reference navigation landmarks during reacquisition and identification.

During the identification task, the identification vehicle determines the fastest reacquisition plan for each contact that must be reacquired. This is either a direct reacquisition in which the contact is systematically covered by the identification sensor, or a two-step approach in which a pre-reacquisition track is executed first before

• identification. The pre-reacquisition track is used to find

back objects detected by the search vehicle, and use those as a reference to correct its own position with. This requires that the identification vehicle is also equipped with an auxiliary sensor to do object detection with. The two plans are computed taking into account the vehicle’s

• wn position uncertainty as well as that of the detected
• contact. Figure 4 shows the result of two search and

identify missions in which detected contacts needed to be reacquired and identified. In one mission the two-step approach was used (left); in the other mission the direct approach was enforced (right). Depending on the position uncertainties, the two-step approach reduces the identification time significantly. In Figure 5 one of the identification results for the two-step reacquisition is depicted: a stitched video recording of one of the identification tracks, together with the single camera frame catching the dummy object.

• Fig. 4. Overview of two executed search and identify missions. The left mission shows the AUV tracks executed during a two-step

search and identify mission where landmarks were used to improve the relative position accuracy between the vehicle and the contact to identify. The cyan line indicates the pre-reacquisition track. In the right mission a direct identification is enforced in which the contacts are systematically covered with the identification sensor, based on the vehicle’s and contact’s location uncertainties. The difference in traversed distance during the identification legs is clearly observable.

• Fig. 5. Identification result for one of the deployed dummy targets. The left image shows the stitched camera image of the concerning

identification leg. The right image shows the exact camera frame in which the contact resides.

Conclusions

With a collaborative autonomy testbed, concepts and the required technology for naval stand-off MCM operations have been developed and experimentally demonstrated. The developed technology includes:

• Self-awareness of system performance for sensor

management;

• A capability for target reacquisition in the presence of

navigation and localization uncertainties applicable in limited underwater visibility conditions

• Collaborative autonomy in conditions with low-

bandwidth communications. The technology development and demonstrations provide relevant insights in the feasibility of naval stand-off MCM concepts using collaborative autonomous systems, including command and control aspects, communications, sensor management, navigation, localization and autonomy.

Acknowledgements

The authors acknowledge the Netherlands Defence Materiel Organisation for funding the research.

References

[1] A.L. van Velsen, I. Mulders, M.W.G. van Riet, L.A. te Raa, Mission management for stand-off naval MCM operations, UDT 2020, Rotterdam. [2] J.J.M. van de Sande, M.W.G. van Riet, A.J. Duijster,

• R. van Vossen, Autonomous multi-vehicle contact

reacquisition using feature-based navigation and in- situ adaptive path planning for AUVs, OCEANS 2019, Marseille.

Author/Speaker Biographies

Robbert van Vossen is senior scientist at TNO. He received his MSc and PhD in seismology from Utrecht University, The Netherlands, and is with TNO since 2007. His main fields of expertise are anti-submarine warfare and naval mine countermeasures. Guus Beckers is senior consultant at TNO. He received his PhD from Erasmus University in Rotterdam. At TNO, Guus coordinates research for naval mine countermeasures. Jeroen van de Sande received the MSc degree in Electrical Engineering at the Delft University of Technology, The Netherlands. Since 2012 he is a Research Scientist within the Acoustics and Sonar Department at the Netherlands Organisation for Applied Scientific Research (TNO), The Hague, The Netherlands, where he works on autonomous mine hunting and sonar processing.