Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

Abstract

This paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach in order to have constraint-based backward non-parallel (perpendicular and diagonal) parking maneuvers capable of dealing with moving pedestrians and, if necessary, performing multiple maneuvers. Our technique relies solely in sensor data expressed relative to the vehicle and therefore no localization is inherently required. Since the proposed approach does not plan any path and instead the controller maneuvers the vehicle directly, the classical path planning related issues are avoided. Real experimentation validates the effectiveness of our approach.

Benchmarking Nonlinear Model Predictive Control with Input Parameterizations

“…One way to simplify the optimization would be to allow only the first N c ≤ N p control samples to be freely allocated in the optimization [15], [16], with the remaining N p − N c samples set to the same value of u Nc−1 (fig. 1(a)).…”

Investigating the Performances of Control Parameterizations for Nonlinear Model Predictive Control

“…One common practice consists in allowing only the first N c ≤ N p control samples to be freely allocated in the optimization [18], [19]. The remaining N p − N c samples are instead set to the same value of u Nc−1 , as illustrated in fig.…”

Parking Pose Generation for Autonomous Freight Collection by Pallet Handling Car-like Robot

“…To speed up the development and integration process with the real experimental vehicle, Robot Operating System (ROS)-based autonomous vehicle's software architecture (called ICARS (Software being developed at LS2N (Laboratoire des Sciences du Numérique de Nantes) www.ls2n.fr (accessed on 15 July 2021)) will be exploited (Figure 5). Considering the task to be accomplished, a particular interest is placed on the Multi-Sensor-Based Predictive Controller (MSBPC) used for parking [31]. As the name of the approach suggests, the parking controller is based on a combination of Model Predictive Control (MPC) and Multi-Sensor-Based Control in order to perform safe (collision-free) parking operations that rely solely on locally perceived sensor features signals and without needing to explicitly plan any path.…”

“…The interaction model described in [31] is used in order to predict the evolution of the sensor features s mp over a finite horizon N p . The cost function is to be minimized with respect to a control sequence ṽr over N p and depends mainly on the difference between s d and s mp .…”

“…As discussed earlier in Section 3, ROS environment-based software architecture ICARS is exploited to speed up the actual experiments. Using the already developed parking schemes within the software architecture [8,9,31], we can park the vehicle in the designated parking pose. The already built parking algorithms are sufficient for maneuvering the vehicle within the parking pose generated autonomously for the freight collection.…”