4 research outputs found
An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics
A robotic swarm that is required to operate for long periods in a potentially
unknown environment can use both evolution and individual learning methods in
order to adapt. However, the role played by the environment in influencing the
effectiveness of each type of learning is not well understood. In this paper,
we address this question by analysing the performance of a swarm in a range of
simulated, dynamic environments where a distributed evolutionary algorithm for
evolving a controller is augmented with a number of different individual
learning mechanisms. The learning mechanisms themselves are defined by
parameters which can be either fixed or inherited. We conduct experiments in a
range of dynamic environments whose characteristics are varied so as to present
different opportunities for learning. Results enable us to map environmental
characteristics to the most effective learning algorithm.Comment: In GECCO 201
A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics
This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA.Firstly, mEDEA is extended with an explicit relative fitness measure while still maintaining the distributed nature of the algorithm. Two ways of using the relative fitness are investigated: influencing the spreading of genomes and performing an explicit genome selection. Both methods lead to an improvement in the swarm’s ability to maintain energy over longer periods.Secondly, a communication energy model is derived and introduced into the simulator to investigate the influence of accounting for the costs of communication in the distributed evolutionary algorithm where communication is a key component.Thirdly, a method is introduced that relates environmental conditions to a measure of the swarm’s behaviour in a 3-dimensional map to study the environment’s influence on the emergence of behaviours at the individual and swarm level. Interesting regions for further experimentation are identified in which algorithm specific characteristics show effect and can be explored.Finally, a novel individual learning method is developed and used to investigate how the most effective balance between evolutionary and lifetime-adaptation mechanisms is influenced by aspects of the environment a swarm operates in. The results show a clear link between the effectiveness of different adaptation mechanisms and environmental conditions, specifically the rate of change and the availability of learning opportunities
Security, privacy and safety evaluation of dynamic and static fleets of drones
Inter-connected objects, either via public or private networks are the near
future of modern societies. Such inter-connected objects are referred to as
Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of
such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such
vehicles are prophesied to take on multiple roles involving mundane to
high-sensitive, such as, prompt pizza or shopping deliveries to your homes to
battlefield deployment for reconnaissance and combat missions. Drones, as we
refer to UAVs in this paper, either can operate individually (solo missions) or
part of a fleet (group missions), with and without constant connection with the
base station. The base station acts as the command centre to manage the
activities of the drones. However, an independent, localised and effective
fleet control is required, potentially based on swarm intelligence, for the
reasons: 1) increase in the number of drone fleets, 2) number of drones in a
fleet might be multiple of tens, 3) time-criticality in making decisions by
such fleets in the wild, 4) potential communication congestions/lag, and 5) in
some cases working in challenging terrains that hinders or mandates-limited
communication with control centre (i.e., operations spanning long period of
times or military usage of such fleets in enemy territory). This self-ware,
mission-focused and independent fleet of drones that potential utilises swarm
intelligence for a) air-traffic and/or flight control management, b) obstacle
avoidance, c) self-preservation while maintaining the mission criteria, d)
collaboration with other fleets in the wild (autonomously) and e) assuring the
security, privacy and safety of physical (drones itself) and virtual (data,
software) assets. In this paper, we investigate the challenges faced by fleet
of drones and propose a potential course of action on how to overcome them.Comment: 12 Pages, 7 Figures, Conference, The 36th IEEE/AIAA Digital Avionics
Systems Conference (DASC'17