2,750 research outputs found
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Fish in schooling formations navigate complex flow-fields replete with
mechanical energy in the vortex wakes of their companions. Their schooling
behaviour has been associated with evolutionary advantages including collective
energy savings. How fish harvest energy from their complex fluid environment
and the underlying physical mechanisms governing energy-extraction during
collective swimming, is still unknown. Here we show that fish can improve their
sustained propulsive efficiency by actively following, and judiciously
intercepting, vortices in the wake of other swimmers. This swimming strategy
leads to collective energy-savings and is revealed through the first ever
combination of deep reinforcement learning with high-fidelity flow simulations.
We find that a `smart-swimmer' can adapt its position and body deformation to
synchronise with the momentum of the oncoming vortices, improving its average
swimming-efficiency at no cost to the leader. The results show that fish may
harvest energy deposited in vortices produced by their peers, and support the
conjecture that swimming in formation is energetically advantageous. Moreover,
this study demonstrates that deep reinforcement learning can produce navigation
algorithms for complex flow-fields, with promising implications for energy
savings in autonomous robotic swarms.Comment: 26 pages, 14 figure
Optimizing collective fieldtaxis of swarming agents through reinforcement learning
Swarming of animal groups enthralls scientists in fields ranging from biology
to physics to engineering. Complex swarming patterns often arise from simple
interactions between individuals to the benefit of the collective whole. The
existence and success of swarming, however, nontrivially depend on microscopic
parameters governing the interactions. Here we show that a machine-learning
technique can be employed to tune these underlying parameters and optimize the
resulting performance. As a concrete example, we take an active matter model
inspired by schools of golden shiners, which collectively conduct phototaxis.
The problem of optimizing the phototaxis capability is then mapped to that of
maximizing benefits in a continuum-armed bandit game. The latter problem
accepts a simple reinforcement-learning algorithm, which can tune the
continuous parameters of the model. This result suggests the utility of
machine-learning methodology in swarm-robotics applications.Comment: 6 pages, 3 figure
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges
Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System
Optimum control strategies for maximum thrust production in underwater undulatory swimming
Fish, cetaceans and many other aquatic vertebrates undulate their bodies to
propel themselves through water. Numerous studies on natural, artificial or
analogous swimmers are dedicated to revealing the links between the kinematics
of body oscillation and the production of thrust for swimming. One of the most
open and difficult questions concerns the best kinematics to maximize this
later quantity for given constraints and how a system strategizes and adjusts
its internal parameters to reach this maximum. To address this challenge, we
exploit a biomimetic robotic swimmer to determine the control signal that
produces the highest thrust. Using machine learning techniques and intuitive
models, we find that this optimal control consists of a square wave function,
whose frequency is fixed by the interplay between the internal dynamics of the
swimmer and the fluid-structure interaction with the surrounding fluid. We then
propose a simple implementation for autonomous robotic swimmers that requires
no prior knowledge of systems or equations. This application to aquatic
locomotion is validated by 2D numerical simulations
- …