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

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

Tracking-error

model-based predictive control for mobile robots in real tim

Effective parametrization of low order Bézier motion primitives for continuous-curvature path-planning applications

We propose a new parametrization of motion primitives based on Bézier curves that suits perfectly path-planning applications (and environment exploration) of wheeled mobile robots. The individual motion primitives can simply be calculated taking into account the requirements of path planning and the constraints of a vehicle, given in the form of the starting and ending orientations, velocities, turning rates, and curvatures. The proposed parametrization provides a natural geometric interpretation of the curve. The solution of the problem does not require optimization and is obtained by solving a system of simple polynomial equations. The resulting planar path composed of the primitives is guaranteed to be C2 continuous (the curvature is therefore continuous). The proposed primitives feature low order Bézier (third order polynomial) curves. This not only provides the final path with minimal required turns or unwanted oscillations that typically appear when using higher-order polynomial primitives due to Runge’s phenomenon but also makes the approach extremely computationally efficient. When used in path planning optimizers, the proposed primitives enable better convergence and conditionality of the optimization problem due to a low number of required parameters and a low order of the polynomials. The main contribution of the paper therefore lies in the analytic solution for the third-order Bézier motion primitive under given boundary conditions that guarantee continuous curvature of the composed spline path. The proposed approach is illustrated on some typical scenarios of path planning for wheeled mobile robots

Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search

The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function combining the Dijkstra-based discrete static potential field evaluation with bilinear interpolation is proposed. The necessary modifications of the bilinear interpolation method are developed to make it applicable to the path-planning application. The effect is that the strategy makes it possible to solve the problem of the local minima, to generate smooth paths with moderate computational complexity, and at the same time, to largely preserve the product of the computationally intensive static plan. To cope with detected changes in the environment, a simple planning strategy is applied, bypassing the static plan with the solution of the A* algorithm to cope with dynamic discoveries. Results from several test environments are presented to illustrate the advantages of the developed navigation model

Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion

This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator

VISION SYSTEM DESIGN FOR MOBILE ROBOT TRACKING

This chapter introduces a complete thematic of vision system for mobile robot tracking and control. It consists of a global vision system for estimation of positions and orientations of mobile robots and methods for improvement of bad operating conditions such as noise, camera lens distortion and non-uniform illumination. The basic operation of a vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find the corresponding regions belonging to one of the classes. In the second step, all the regions are examined. A selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty of the used approach is focused on optimization of the processing time needed to finish the estimation of possible object positions. Further on an approach to improve an already existing vision system performance under bad operating conditions is presented. Some fundamentals and solutions to accompanying problems in vision system design for mobile robot tracking are presented. Besides methods for filtering and improvement of identified noisy data the two main factors which deteriorate the performance are dealt with, namely, non-uniform illumination and camera lens distortion. For the former the problem area and its origins are focused on and a solution for its compensation by applying multiplicative component defined by illumination plain is given. The latter consists of two steps. In the first, radial lens distortion fundamentals are discussed. The suggested solution for its verification is realized by a geometry model of lens projection. The second step covers the perspective distortion originating from the tilt of the camera. For its correction an efficient and robust method of vanishing point detection is applied. Both correction methods contribute to a vision system performance if implemented in the appropriate manner