Deep Reinforcement Learning for Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking

Srikonda, Sandeep; Norris, William P.; Nottage, Dustin; Soylemezoglu, Ahmet

doi:10.3390/robotics11050095

Cited by 4 publications

(1 citation statement)

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“…In [89], the authors successfully implemented the DDPG algorithm for skid-steered wheeled robot trajectory tracking. The RL was applied to training the agent in an unsupervised manner.…”

Section: Reinforcement Learning For Motion Control and Other Tasksmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

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“…In [89], the authors successfully implemented the DDPG algorithm for skid-steered wheeled robot trajectory tracking. The RL was applied to training the agent in an unsupervised manner.…”

Section: Reinforcement Learning For Motion Control and Other Tasksmentioning

confidence: 99%