Autonomous leader-follower formation control of non-holonomic wheeled mobile robots by incremental path planning and sliding mode augmented tracking control

Abstract

This paper presents a novel robust and autonomous formation control scheme for wheeled mobile robots in the leader-follower formation control framework considering their non-holonomic constraints. In the proposed formation control scheme, the leader robot of the group plans its path of navigation autonomously in a cluttered environment by employing incremental path planning by modified artificial potential field. Then, the follower robots in the group plan their path in order to follow the leader robot by maintaining a particular formation using the separation-bearing (l-ψ) control. Then the formation control problem has been transformed into a trajectory tracking control problem. The kinematic control component of the tracking controller provides the necessary velocity input for eliminating the non-holonomic constraints, whereas, the sliding mode augmented robust trajectory tracking control component minimises the effects of nonlinearities, model uncertainties, parameter variations, and disturbances. The effectiveness of the proposed control law has been established by simulation studies.

A comprehensive review of the latest path planning developments for multi-robot formation systems

“…Finally, the literature has shown a larger number of papers proposing UGV leader-follower systems compared to USV and UAV systems. Variety path planning techniques were applied, including gridbased [50], potential field [51][52][53], optimization and machine learning [54][55][56][57][58], and other techniques [59][60][61]. Examining localization tasks in uncertain environments is the objective of the authors in ref.…”

“…On the other hand, the proposed leader-follower techniques in refs. [51][52][53] have used the potential field method as a path planning technique. In addition to the potential field method, the asexual reproduction optimization method has been used in ref.…”

Neural-Adaptive Finite-Time Formation Tracking Control of Multiple Nonholonomic Agents With a Time-Varying Target

“…8 has been analyzed as the example. Firstly, according to the equations (5), (27) and the values of α 2 , β 2 , m 3 , n 3 , p 3 , q 3 , we can know that v 6 → v * 6 at about 2.4 seconds. Secondly, we can obtain that v * 6 →v 6 at about 1.6 seconds based on the equation 5, the definition of v * 6 and the values of α 61 , β 61 , m 0 , n 0 , p 0 , q 0 .…”

“…Many effective control approaches have been designed to regulate MNASs [20]- [22]. These methods can generally be divided into three classes, namely, the behavior-based approaches [23], [24], the virtual-structure-based approaches [25], [26], and the leaderfollowing-based approaches [27]- [29]. The behavior-based approaches are designed mainly based on the employment of reactive behaviors to achieve formation tracking.…”

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

“…Many tools, such as the direct Lyapunov method, input-output linearisation, backstepping, graph theoretic techniques, the artificial potential field (APF) method, etc., are used in the leader-follower method (11) . Among these techniques, the potential function method has been the focus of many researchers due to simple design and feasibility for real-time system control, although it suffers from the local minima problem (12) .…”