Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation

Abstract

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

“…This approach often results in longer paths compared to the Bezier curve, since these curves only pass through the first and last control points [34]. The Bezier curve is used in various fields, such as face recognition [35], medical imaging [36], computer graphics and animation [37], path planning for robots [38][39][40], etc. The fifth-order Bezier curve is formed for the path of the wheeled mobile robot with the constraints of speed, acceleration, and jerk [41].…”

“…. (38) where ε and κ are determined by fuzzy mathematical methods; the constant coefficients η 1 and η 2 are selected based on the motion characteristics of the tracked agricultural machinery. The fuzzy approach is used to normalize the switching function s. The input of the fuzzy sliding mode variable structure controller is defined as…”

Adaptive Neural Network Prescribed Time Control for Constrained Multi-Robotics Systems with Parametric Uncertainties

“…1, 0.3, 0.5, 0.2] [0.2, 0.4, 0.6, 0.5] [30, 20,15,22] [20, 10,8,18] [0.1, 0.5] [5, 4] 1 0.1 II [0.1, 0.3, 0.5, 0.2] [0.2, 0.4, 0.6, 0.5] [25, 18,12,20] [18, 13,6,12] [0.08, 0.6] [3, 5] 1.5 0.2 III [0.1, 0.3, 0.2, 0.4] [0.5, 0.6, 0.7, 0.8] [30, 20,15,22] [20, 10,8,18] [0.1, 0.5] [5, 4] 1 0.1…”

“…Multi-robotic systems have extensive applications across various domains [1][2][3][4][5][6][7][8][9], including agriculture [10], healthcare [11], manufacturing automation [12,13], and other fields. In multi-robotic systems, cooperative control primarily includes consensus, formation, and containment control.…”

Advanced Sensors Technologies Applied in Mobile Robot

“…The model and examples with different control scenarios are available online. In [ 19 ], a global navigation function for model predictive control (MPC) of autonomous mobile robots with application to warehouse automation is proposed. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation.…”