A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization

Abstract

This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

Handling Constrained Optimization in Factor Graphs for Autonomous Navigation

“…( 20) corresponds to a regular error factor and is shown with a green square. Compared to our reference [1], we added the third summation, which penalizes changes between consecutive acceleration inputs to reduce the jerk. The variables of the factor graph modeling the optimal control problem are: the states of the robot X t=0:T = [x t , y t , θ t , v t , ϕ t , ω t ] T and the controls u t=0:T −1 .…”

“…Nonlinear Optimization is at the core of many robotics applications across various fields, such as mobile robotics [1], SLAM [16], [18], Structure from Motion (SfM) [22] and calibration [9]. The workflow consists of two stages.…”

“…The core idea of our method is to use the AL method to model a new type of factors which can be directly included in existing unconstrained solvers: the constraint 1 Barbara Bazzana and Giorgio Grisetti are with the Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy {bazzana, grisetti}@diag.uniroma1.it 2 Henrik Andreasson is with the Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden henrik.andreasson@oru.se factors. Handling constraints enlarges the application domain of factor graphs confirming them as a general framework for optimization in robotics.…”

“…The system can recover orientations of the poses from arbitrary initial guesses. The third is MPC of the pseudo-omnidirectional platform presented in [1], which comprises dynamics, velocity and acceleration constraints. In introducing each application, we provide the reader with practical insights on implementation and parameter choices.…”

Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots

“…Andreasson et al [ 3 ] describe a local planning approach for pseudo-omnidirectional vehicles, that is, vehicles that can drive sideways and rotate in place. The local planner, named MSD, is rooted in optimal control theory and relies on the formulation of a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities.…”