Autonomy in Challenging Environments

Key Features

Apply for a share of up to £2 million to develop technologies to broaden the environmental and performance envelope of unmanned autonomous or semi-autonomous systems.

Programme:     DASA

Award:     Share of up to £2 million

Opens: 14th Aug 2019

Closes: 10th Oct 2019

! This scheme is now closed


This Defence and Security Accelerator (DASA) competition is seeking proposals that can provide a step change in the capability of unmanned autonomous military systems to operate in challenging environments.

This call is funded through the MOD’s Chief Scientific Advisor’s Research Programme’s Autonomy Incubator project that aims to: identify and develop underpinning research and technologies to support the development and fielding of unmanned systems across defence which may be matured through the Autonomy and other research and development programmes.

Unmanned, autonomous and semi-autonomous systems have potential applications across many military capability areas and civilian operations and are expected to be increasingly deployed by the UK Armed Forces over the next few decades. Many autonomous systems have been developed and optimised in ideal conditions. However, future military operations are anticipated to be in environments that are challenging both from a physical and electromagnetic (EM) perspective, affecting the efficiency and effectiveness of current autonomy technologies. Consequently, there is a need for technologies to enhance the performance of autonomous systems in challenging environments to support future military operations.

Funding Costs

The total funding available for this Phase 1 competition is £2 million, but individual proposals cannot exceed £100k.

If successful, contracts will be awarded for a duration of 6 months.

Phase 1 aims to understand the feasibility, impact and military application of the innovation; for potential further development in Phase 2.

Additional funding is expected to be available for further phases. Please note any further phases will be open to applications from all suppliers and not just those that submitted Phase 1 successful bids.


This competition is seeking technologies to broaden the environmental and performance envelope of unmanned autonomous or semi-autonomous systems to include:

  • unmanned underwater vehicle (UUV)
  • unmanned surface vehicle (USV)
  • unmanned ground vehicle (UGV)
  • unmanned air system (UAS)
  • or hybrid systems

The challenging environmental conditions within scope are:

  • high winds (such as gust effects and urban turbulence)
  • heavy precipitation (such as rain, snow, blizzards and icing)
  • high dynamic range illumination (including changes to UV, and night vision)
  • water dynamics (such as currents and visibility)
  • temperature (such as temperature extremes and fluctuations between extremes)
  • sudden and enduring pressure or acoustic extremes underwater
  • intense flashes of light (including infrared and ultraviolet)
  • variable salinity
  • dense vegetation (including flora and fauna)
  • extreme and diverse terrains (such as variability in traction and elevation)
  • high-obstacle environments (such as within caves and buildings)
  • congested and contested EM environments (including radio frequency (RF) emissions)
  • GPS denied environments

Any solutions proposed must not erode the core benefits of the existing unmanned autonomous or semi-autonomous systems which include:

  • persistence: unmanned systems should be able to operate independently for long periods and/or over long ranges either singularly or through an exchange or replacement system. Priority will be given to ideas that will have a low impact on size, weight and power (SWAP), for example, novel structural concepts could be combined with sensing and perception.
  • combat mass: where the unmanned systems increase the sphere of influence through larger numbers of low cost systems. Low cost solutions are sought here.
  • reach: unmanned systems must collectively lead to an increase in effective range of operation which must still be achieved in high risk and physically constrained environments.


Proposals must address one or more of the following three challenge areas associated with the unmanned systems and environmental conditions –

Challenge 1: perception and situational awareness

This challenge seeks technologies to establish and maintain local situational and self-awareness of unmanned autonomous or semi-autonomous systems in adverse environmental conditions or across a spectrum of variable conditions. This requires that the system has the ability to sense, interpret, and understand its local environment, and then respond autonomously to that understanding appropriately.

Areas of interest include:

  • novel or alternative sensing techniques (such as EM, acoustic, seismic, flow, novel electro-optic (EO) and polarisation sensing)
  • imaging techniques that can operate in lowlight and high illumination environments
  • techniques for dynamic sensor and platform stabilisation in turbulent or rough environments (through either physical or processing means)
  • techniques for the abstraction of information to expand the operational window, with minimal human participation (including effects of sensor data uncertainty on real-time interpretation of the information)
  • techniques or materials to minimise sensor obscuration due to moisture or contaminants (causes could include rain, mist, sea spray, surfacing, and rapid temperature changes)
  • novel antennas (including sensor pre-processing); directional communications and other technologies aimed at reducing the vulnerability of autonomous systems.

This challenge is not just about better sensors, it is about situational / self-awareness, and therefore any sensor-based solutions should come with the requisite processing to demonstrate the required capability enhancement.

Challenge 2: mobility

There is an aspiration to maintain the freedom of mobility of autonomous systems as conditions deteriorate, particularly in dynamic, uncertain and cluttered environments. This challenge seeks solutions that will allow autonomous and semi-autonomous platforms to withstand the effects of challenging environmental conditions throughout their missions.

DASA are interested in innovative technologies which address this, such as anisotropic materials applications, soft robotics, embodied artificial intelligence (AI) or other novel methods to increase autonomous systems’ ability to respond or adapt to environmental challenges. Priority will be given to technologies that support the benefits and priorities identified in the scope. This challenge also facilitates the application of Challenge 1 technologies to maintain mobility.

Challenge 3: maintaining effective human-machine partnerships

At the core of future military advantage will be the effective integration of humans, AI and robotics into military systems – human-machine teams. Except for UUVs, research concepts for autonomous and semi-autonomous systems have relied on the ability to maintain constant communication between the human operator and the unmanned platform. During operations in the challenging environments described, maintaining effective human-machine teams is a difficult when communications are not always guaranteed.

This challenge seeks proposals that address human-machine teaming when the ability to communicate with the unmanned systems is limited, fleeting or not at all possible for extended periods.

Areas of interest include:

  • pre-mission planning: the collaborative development of mission plans, using a mixed initiative approach between the human and system. Work in this area should focus on an anticipated loss of communications and the development of suitable contingencies and strategies.
  • desynchronised operations (both deliberate and unplanned): when the communications link is lost, how the autonomous system continues to conduct the mission effectively and safely in line with the human operator’s intent, while dynamically adapting to changes in the external environment and optimising the opportunities to re-establish communications. Can the human operator predict what the system will do and anticipate when the autonomous system might resume communications?
  • resynchronisation of the human-machine team: when communications are re-established how the human-machine team quickly share what they have been doing, their respective situational awareness and any updates to future plans.