Comfort-Oriented Motion Planning Using Deep Reinforcement Learning

Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host...

Learning a guidance policy to navigate among dynamic agents in constrained environments with continual reinforcement learning

Mobile robots that operate in human environments require the ability to safely navigate among humans and other obstacles. Existing approaches use Deep Reinforcement Learning (DRL) to obtain safe robot behavior in such environments, but do not ensure collision avoidance or trajectory feasibility. This issue is solved by methods combining DRL with...

Safe Navigation in Dense Traffic Scenarios using Reinforcement Learning as Global Guidance for a Model Predictive Controller

The successful integration of autonomous vehicles (AVs) in human environments is highly dependent on their ability to navigate safely and timely through dense traffic conditions. Such conditions involve a diverse range of human behaviors, ranging from cooperative (willing to yield) to non-cooperative human drivers (unwilling to yield) that need...

End-to-End Motion Planning: A Data Driven Approach for Mobile Robot Navigation

A lot research has been conducted in the field of autonomous navigation of mobile robots with focus on Robot Vision and Robot Motion Planning. However, most of the classical navigation solutions require several steps of data pre-processing and hand tuning of parameters, with separate modules for vision, localization, planning and control. All...