Lifelong Learning for Vision based AUV Control

Lead Research Organisation: Heriot-Watt University
Department Name: S of Mathematical and Computer Sciences


Principal goal for project:
The main objective is to design a low-level controller for autonomous underwater vehicles (AUVs) using learning frameworks. The algorithm should be portable across multiple robots, The controller should adapt quickly to any change in the dynamics (increase of payload, current disturbances, damage to the robot, thruster efficiency decay, etc...)
Research project:
Control of AUVs is a difficult task mainly due to the non-linearity of the dynamics in the underwater domain. Classic control approaches, such as PIDs or sliding mode controller, usually operate at a low speed and require a lot of expert knowledge to tune the different parameters. The parameters usually work for one specific configuration of a robot and tuning is necessary every time the robot configuration changes.
To overcome those limitations, this research will look at learning-based methods and will try to combine them with classic control methods. The learning-based methods allow using data collected during robot operation to improve the controller. Combining those methods with classic control approaches allow us to speed up the learning process as most of the recent learning methods require a lot of data and learning time.

Planned Impact

The Centre will have immediate short-term impacts on people skills and innovation pipeline, alongside key advances in scientific knowledge and techniques in Robotics and Autonomous Systems (RAS). With the strength of the programme's training emphasis on safety and responsible research and innovation (RI), we also target longer term economic and societal benefits.

Economy: It is estimated that the application of advanced robotics could generate a potential worldwide economic impact of $1.7-4.5 trillion by 2025 per year by 2025 (McKinsey). Over the last 5 years, the UK has witnessed significant new investments in robotics from both Government with the £4.7B Industrial Strategy Challenge Fund (ISCF), spawning parallel investments from industry. An example is the EPSRC £18M investment in the EPSRC ORCA Hub in RAS, led by Heriot-Watt, leveraging a further £18.5M industry investment. The potential economic impact of RAS, however, is hampered by a massive skills shortage, which the proposed CDT-RAS would address. The new jobs created by these investments will require highly specialised, yet interdisciplinary and industrially relevant skill-sets. The CDT-RAS is well positioned to supply the UK workforce in this growing area, through strong links with industry through its extensive CDT-RAS Project Partners network and through a training emphasis on 'innovation-ready' graduates. For example, our CDT-RAS students will have the opportunity to grow into industrial leaders of tomorrow through direct experience and company placements, as well as, through the CDT's extensive support for commercialisation and start-ups.

Society: Robotic and autonomous systems have already been identified by the UK government as a key component to enable safer working conditions for 'dull, dirty and dangerous' tasks in extreme and challenging environments such as offshore, nuclear, mining, and space. Moreover, there are disruptive opportunities for RAS to contribute to cost-effective and safer construction, transport, and manufacturing and improved quality-of-life through healthcare and assisted living. CDT-RAS training focuses on interdisciplinary, cross-cutting, yet responsible research and innovation to allow our future leaders to develop techniques and technologies that will have impacts in new areas, beneficially improving society beyond what we can already imagine. We will develop autonomous systems and AI enablers that are transparent to developers and end-users alike. This will allow robots and machines to work seamlessly in society both individually and in teams and comply with regulations, such as the EU General Data Protection Regulations (GDPR) and emerging IEEE standards, such as P7001 for Transparency for Autonomous Systems (for which Centre academics are members of the working group).

Science: CDT-RAS students will benefit from i) a critical mass of over 50 experienced supervisors, ii) the brand new facilities of the £27.5M National ROBOTARIUM and earlier £8M EPSRC equipment investments, as well as, iii) opportunities for international scientific and industrial lab placements. The Centre will realise scientific advancement and impact, crucial to enabling safe interaction between RAS, humans and the environment, including soft robotics, bio-inspired systems, human-robot interaction, swarms and collaborative robotics including human-robot teaming, sensing, embedded control, multi-agent decision making and maritime field robotics. The impact of the resultant research will be strengthened through top-venue publications and conference presentations, utilising student presentation/writing skills honed during the CDT-RAS training. Impact will also come through outreach such as international student robot competitions, public engagement activities such as science festivals and CDT-RAS hosted international researcher visitors and workshops.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023208/1 01/10/2019 31/03/2028
2270314 Studentship EP/S023208/1 01/10/2019 30/09/2023 Pierre Nicolay