Apart from perception, one of the most fundamental aspects of an autonomous mobile robot is the ability to adequately and safely traverse the environment it operates in. This ability is called Navigation and is performed in a two- or three-dimensional fashion, except for cases where the robot is neither a ground vehicle nor articulated (e.g. robotics arms). The planning part of navigation comprises a global planner, suitable for generating a path from an initial to a target pose, and a local planner tasked with traversing the aforementioned path while dealing with environmental, sensorial and motion uncertainties. However, the task of selecting the optimal global and/or local planner combination is quite hard since no research provides insight on which is best regarding the domain and planner limitations. In this context, current work performs a comparative analysis on qualitative and quantitative aspects of the most common ROS-enabled global and local planners for robots operating in two-dimensional static environments, on the basis of mission-centered and planner-related metrics, optimality and traversability aspects, as well as non-measurable aspects, such as documentation quality, parameterisability, ease of use, etc.
https://link.springer.com/article/10.1007/s10846-019-01086-y
Publications
New publication:On-Road Autonomous Vehicle Navigation In A Dynamic Environment Using Deep Reinforcement Learning, Towards Fuel Consumption Optimization
In this work we explore the application of deep reinforcement learning (DRL) in navigating autonomous vehicles (AVs) within dynamic environments, aiming to optimize fuel efficiency without compromising safety or operational reliability. Focusing on the intricate Read more…