3 research outputs found
Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review
This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or
other autonomous vehicles, are two fundamental guidance problems in robotics.
For many decades, they have been subject to academic study, leading to a vast
number of proposed approaches. However, they have mostly been treated as
separate problems, and have typically relied on non-linear first-principles
models with parameters that can only be determined experimentally. The rise of
Deep Reinforcement Learning (DRL) in recent years suggests an alternative
approach: end-to-end learning of the optimal guidance policy from scratch by
means of a trial-and-error based approach. In this article, we explore the
potential of Proximal Policy Optimization (PPO), a DRL algorithm with
demonstrated state-of-the-art performance on Continuous Control tasks, when
applied to the dual-objective problem of controlling an underactuated
Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows
an a priori known desired path while avoiding collisions with other vessels
along the way. Based on high-fidelity elevation and AIS tracking data from the
Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's
performance in challenging, dynamic real-world scenarios where the ultimate
success of the agent rests upon its ability to navigate non-uniform marine
terrain while handling challenging, but realistic vessel encounters
Research on UUV Obstacle Avoiding Method Based on Recurrent Neural Networks
In this paper, we present an online obstacle avoidance planning method for unmanned underwater vehicle (UUV) based on clockwork recurrent neural network (CW-RNN) and long short-term memory (LSTM), respectively. In essence, UUV online obstacle avoidance planning is a spatiotemporal sequence planning problem with the spatiotemporal data sequence of sensors as input and control instruction to motion controller of UUV as output. And recurrent neural networks (RNNs) have proven to give state-of-the-art performance on many sequence labeling and sequence prediction tasks. In order to train the networks, a UUV obstacle avoidance dataset is generated and an offline training and testing is adopted in this paper. Finally, the proposed two types of RNN based online obstacle avoidance planners are compared in path cost, obstacle avoidance planning success rate, training time, time-consumption, learning, and generalization, respectively. And the good performance of the proposed methods is demonstrated with a series of simulation experiments in different environments