Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning

Abstract

Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot’s speed to maintain a flexible formation with the leader robot.

Trusted Data Storage Architecture for National Infrastructure

“…Once all named entities in the text are extracted, they can be linked to the set corresponding to the actual entities, from early dictionary and rule-based methods to statistical machine learning methods. The methods based on statistical machine learning mainly include Hidden Markov Model (HMM), Maximum Entropy Model (MEM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so on [ 15 , 16 , 17 , 18 ]. Conditional field [ 19 ] is the mainstream model of NER at present.…”

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

“…• Transport. In this field, several studies focus on process improvement, such as speed and accuracy in lane changing maneuvers when driving on highways [97], driving terrestrial vehicles on rural roads [98], robots learning routes through linguistic decision trees [99], and methods used in biped robot walking processes [100,101].…”

Leader–follower formation control based on non-inertial frames for non–holonomic mobile robots

“…Despite the fact that there are many research related to performing a leader-follower formation or a platoon formation, the main problem is tracking the same path that the leader robot performs, it has been shown that using a fixed distance between robots is not the best method to achieve this objective. For that reason, other methods are proposed, such as speed control by using machine learning [ 18 ], where by doing a flexible formation, they maintain a safe varying distance between robots and follow the same straight path, but this is not so useful to perform in curved paths; in [ 19 ] they use four different frameworks to perform the platoon formation by controlling velocity, distance, geometry formation, longitudinal and lateral velocities, but this is only used in straight lines and merging operations. In [ 20 ] a longitudinal and lateral control strategy is proposed, but the steering strategy is once again only used to change lines, and they proposed that can be extended into a broader driving scenario for future work.…”