Visual Reward for Autonomous Driving

Artificial Intelligence (AI) is seen to show wide adaptation possibilities in many fields, and therefore it is used to solve more and more complex problems. One subfield of it is reinforcement learning, which tries to learn a robot to solve a specified task with a given reward function. The reward function is used to tell the robot, how valuable different actions are in different states. Defining a reward function for a robot in open spaces can be difficult, and one example of this is teaching a robot to drive a car. In these situations, imitation learning and Inverse Reinforcement Learning (IRL) can offer a solution by turning the problem upside down by creating the reward function from expert demonstrations. These can contain any kind of data that the robot uses to learn the correct policy for the task. This research studies the possibility to use a visual reward for autonomous driving. Driving simulator Carla is used for creating the training data and running the experiments. Expert demonstrations contain driving videos and control data, and latest research results [1] are used for decreasing the required training data to only a dozen of expert demonstrations. The experiments showed that a visual reward can be used for autonomous driving, when the task is simple. More research should be done for finding working parameters for longer tasks

Applications of artificial intelligence in ship berthing: A review

Ship berthing operations in restricted waters such as ports requires the accurate use of onboard-vessel equipment such as rudder, thrusters, and main propulsions. For big ships, the assistance of exterior supports such as tugboats are necessary, however with the advancement of technology, we may hypothesize that the use of artificial intelligence to support ship berthing safely at ports without the dependency on the tugboats may be a reality. In this paper we comprehensively assessed and analyzed several literatures regarding this topic. Through this review, we seek out to present a better understanding of the use of artificial intelligence in ship berthing especially neural networks and collision avoidance algorithms. We discovered that the use of global and local path planning combined with Artificial Neural Network (ANN) may help to achieve collision avoidance while completing ship berthing operations

Applications of artificial intelligence in ship berthing: A review

855-863Ship berthing operations in restricted waters such as ports requires the accurate use of onboard-vessel equipment such as rudder, thrusters, and main propulsions. For big ships, the assistance of exterior supports such as tugboats are necessary, however with the advancement of technology, we may hypothesize that the use of artificial intelligence to support ship berthing safely at ports without the dependency on the tugboats may be a reality. In this paper we comprehensively assessed and analyzed several literatures regarding this topic. Through this review, we seek out to present a better understanding of the use of artificial intelligence in ship berthing especially neural networks and collision avoidance algorithms. We discovered that the use of global and local path planning combined with Artificial Neural Network (ANN) may help to achieve collision avoidance while completing ship berthing operations

Vision-based anticipatory controller for the autonomous navigation of an UAV using artificial neural networks

© 2014 Elsevier B.V. A vision-based anticipatory controller for the autonomous indoor navigation of an unmanned aerial vehicle (UAV) is the topic of this paper. A dual Feedforward/Feedback architecture has been used as the UAV's controller and the K-NN classifier using the gray level image histogram as discriminant variables has been applied for landmarks recognition. After a brief description of the UAV, we first identify the two main components of its autonomous navigation, namely, the landmark recognition and the dual controller based on cerebellar system of living beings, then we focus on the anticipatory module that has been implemented by an artificial neural network. Afterwards, the paper describes the experimental setup and discusses the experimental results centered mainly on the basic UAV's behavior of landmark approximation maneuver, which in topological navigation is known as the beaconing or homing problem.Peer Reviewe