Sie können Operatoren mit Ihrer Suchanfrage kombinieren, um diese noch präziser einzugrenzen. Klicken Sie auf den Suchoperator, um eine Erklärung seiner Funktionsweise anzuzeigen.
Findet Dokumente, in denen beide Begriffe in beliebiger Reihenfolge innerhalb von maximal n Worten zueinander stehen. Empfehlung: Wählen Sie zwischen 15 und 30 als maximale Wortanzahl (z.B. NEAR(hybrid, antrieb, 20)).
Findet Dokumente, in denen der Begriff in Wortvarianten vorkommt, wobei diese VOR, HINTER oder VOR und HINTER dem Suchbegriff anschließen können (z.B., leichtbau*, *leichtbau, *leichtbau*).
Das Projekt SHINE-Fleet zielt darauf ab, den Logistiksektor zu revolutionieren, indem es Wasserstoffbrennstoffzellen mit automatisierten Antriebstechnologien kombiniert, um die Sicherheit und Effizienz im Schwerlastbetrieb zu verbessern. Ein zentraler Schwerpunkt des Projekts ist das nicht invasive System zur Automatisierung von Andockmanövern, bei dem in der Infrastruktur installierte Wahrnehmungsmodule eingesetzt werden, um eine Automatisierung zu erreichen, ohne das Fahrzeug mit zusätzlichen Sensoren auszustatten. Die Architektur und Kontrollmechanismen des Systems werden detailliert untersucht, einschließlich des Lokalisierungs- und Wahrnehmungsmoduls, der Pfadplanung und der Kontrollmechanismen. Das Potenzial des Projekts, Arbeitsunfälle zu reduzieren und die Rangiereffizienz zu steigern, wird hervorgehoben, was es zu einer vielversprechenden Lösung für die Zukunft des nachhaltigen Güterverkehrs macht.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
In recent years, reducing emissions in Freight-Transport has become a top priority in mobility. This paper presents the methodology for the full control loop of a truck’s powertrain, which has been transformed from a polluting GLP powered ICE to a zero-emission Hydrogen-powered electric powertrain within the SHINE-Fleet project. The adaptation has provided the ECU with increased flexibility to control the dynamics, enabling automated-driven missions like docking. The paper covers infrastructure-based localization for the vehicle, path planning to reach the setpoint, and control implementation with a Model Predictive Controller (MPC).
1 Introduction
In Spain, the transport sector represents more than 40% of energy consumption and is responsible for 27% of direct greenhouse gas (GHG) emissions, which places it in the spotlight for regulatory compliance of the international [1] and national [2] regulatory framework for the reduction of emissions.
According to the Transport and Logistics Observatory (OTLE) [3] in Spain, regarding GHG emissions by road, it is the transport of goods that contributes the most to emissions (\(<10\%\) of the vehicle fleet but around 40% of carbon emissions), and their activity continues increasing.
Anzeige
In a European context of promoting measures for the decarbonization of transport, derived from the commitment to the “zero emissions” objective in 2050 [4], trucks with hydrogen fuel cells aim to become the only possible replacement for petrol-powered trucks in long-haul freight transport.
Thus, the logistics sector has many potential applications for hydrogen propulsion technologies, but the most promising are long-haul trucks between 20 and 40 tons.
In this context, the SHINE-Fleet project -Technological solutions based on Hydrogen for the intelligent and sustainable mobility of autonomous Heavy-duty fleets- is based on research and the collaboration of technologies in a coordinated, structured, and interrelated way, to advance in overcoming the challenges that arise for hydrogen to replace fossil fuels in long-haul heavy transport distance, and take advantage of the electric powertrain to automate repetitive difficult maneuvers, such as docking.
The “SHINE-Fleet” Consortium comprises the synergy provided by 7 partners, joining efforts in human and material resources, generating the proper environment for achieving the objectives set in the Project.
Anzeige
With this preamble, the paper presents a non-invasive system for automating docking maneuvers without adding extra sensors to the vehicle within the SHINE-Fleet project. The system utilizes perception modules installed in the infrastructure to achieve this automation.
Throughout this paper, we will delve into the context of the project in Sect. 2. Then, a description of the system will be presented in Sect. 3, followed by an explanation of the localization and perception module in Sect. 4. The decision-making process for path planning to execute the mission will be explored in Sect. 5, while Sect. 6 will detail the control mechanisms used to follow the setpoint accurately. Finally, Sect. 7 will present the conclusions drawn from this study.
2 Context of the Solution
According to statistics, in Spain, there were 23,569 workplace accidents among drivers and mobile machinery operators in loading and warehouse areas during the first 6 months of 2023 [5].
Partner FMLogistic’s information reveals that, apart from typical loading and unloading operations involving up to 200 trucks daily, there are additional activities like vehicle washing and repair, sometimes even at night or in low visibility conditions.
Hence, this paper aims to explore a cost-effective way to assist the docking maneuver by controlling the truck from infrastructure, improving safety and efficiency compared to only-human-driven operations and help reduce accidents.
3 Description of the Solution
In the project’s development, four strategically placed LiDARs in the infrastructure, as seen in Fig. 1, calculate the head and load positions, as well as surrounding obstacles inside a map. Using this map, a continuous curvature trajectory is generated. The controller then determines accelerator, brake, and steering signals, wirelessly transmitting them to the vehicle’s onboard computer. After safety verification, the onboard computer sends the setpoint via the CAN line for the ECU to act upon the semi-trailer.
So, as in every manual driving loop, it is possible to identify three several modules inside the automated driving loop [6], but three modules are crucial inside this project, as can be seen in Fig. 2:
Localization & Perception: this module is in charge of positioning the vehicle and the obstacles inside the map.
Path planning: this module takes the position of the vehicle and the obstacles on the map and creates a plausible trajectory with speed to perform the docking maneuver.
Control: this module tries to follow the setpoint trajectory by creating the throttle, brake, and steer signals that feed the vehicle system.
In order to know the position, orientation, and pitch angle of both load and head truck, a detection system is developed, using 4 Ouster OS1-32 LIDAR sensors for that purpose. We configure a setup shown in Fig. 1, maximizing the number of points that collide within the lateral parts of the trailer.
Fig. 3.
Representation of each step of the algorithm: plane filtering, clustering and load-head marker from where position, orientation and pitch are detected.
Our method, illustrated in Fig. 3, takes an octomap [7] that contains the environment representation, excluding the truck. A subtraction operation is performed between the merged point cloud of the LIDARs, concatenated, filtered, and aligned before this step, and the octomap, effectively isolating the points that define the truck. Then, the disseminated point cloud passes through a RANSAC [8] algorithm, used to exclude data not related to a plane of the trailer. Following this, we calculate the plane angle. The procedure also includes a clustering process to remove noise and identify the closest point to the wall. By leveraging this angle and the identified minimum point for load and head, their centers are accurately and stably determined.
5 Path Planning
After determining the vehicle’s location, the next step is to establish the route, using a local trajectory connecting the semitrailer’s control point (load’s rear axle) to the destination. As the trajectory is exclusively in reverse for ensuring a smooth and unique parking operation, curvature continuity is crucial for defining feasible trajectories, given the vehicle’s stability in reverse [9]. Then, the controller will optimize these smooth maneuvers to achieve the desired outcome.
To ensure continuity in curvature between the initial and final points, the simplest approach is to generate cubic Bézier curves [10] from the start to the end point, and projecting a straight line from the start or end with its orientation. The trajectory can be described as Eq. 1, depending on the percentage p of the path:
Finally, a and b are parameters greater than 0, which will be adjusted to optimize the total distance of the generated trajectory while limiting the turning radius.
6 Control
The system will be controlled using a PID controller for longitudinal tracking and a Model Predictive Controller (MPC) with a cost function that minimizes the position and orientation of the load for lateral tracking.
6.1 Longitudinal Control
As the maneuver will start from a point that allows the entire trajectory to be covered exclusively in reverse, a velocity setpoint \(v_k\) will be defined for each point of the trajectory k, given by the minimum absolute value between a limit value \(v_{max}\) and a constant c multiplied by the distance \(d_{k,end}\) from point k to the last point of the trajectory.
Next, a PID controller will be responsible for obtaining a torque setpoint \(\tau \) to reach the velocity setpoint. Finally, knowing the accelerator torque limits \(\tau _{th\ max}\) and brake torque limits \(\tau _{br\ max}\), the torque setpoint will be normalized to obtain the pedal signals.
6.2 Lateral Control
To define the MPC controller used for lateral tracking, a cascade bicycle model[12] is employed, where the front head acts as the front wheel. With this approach, using the variables described in Fig. 4, the discrete model equations in the spatial domain[11] can be obtained based on the steering angle \(\delta \):
Once the system is defined, a cost function is created that takes into account the position and orientation of the load while minimizing the error in the rear axle position \(e_{p l}\) and orientation \(e_{\psi l}\) tracking with associated weights w. Thus, the following system cost function is obtained:
Finally, steering angle limits are defined as constraints on the control signal, as well as limits for the joint to avoid jackknifing. The optimization problem is then solved, returning the steering angle that should be applied at each moment.
7 Conclusions
Throughout this paper, we have elucidated the control strategy aimed at automating the docking maneuver of articulated vehicles in a minimally invasive manner. As a result, a single logistics center can compute trajectories for multiple trucks without necessitating expensive vehicle modifications. This approach holds the potential to mitigate workplace accidents during loading and unloading, while concurrently enhancing the overall maneuvering efficiency.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Acuerdo de Paris de las Naciones Unidas (2015); Cumbre del Clima de 2018 en Katowice y Agenda 20130 de Desarrollo Sostenible de las Naciones Unidas, el Paquete de Invierno de la Comisión Europea (2019) y European Green Deal (2020)
2.
Anteproyecto de Ley de Cambio Climático y Transición Energética; Plan Nacional Integrado de Energía y Clima (PNIEC) 2021-2030; estrategia de Transición Justa, Estrategia de Des-carbonización a 2050
3.
Informe Anual del Observatorio de Transporte y Logística de España (2019)
4.
CarbonBrief Octubre (2018)
5.
cadenadesuministro.es: El número de accidentes laborales en el transporte y la logística registrados hasta junio crece un 3,86% anual
6.
Lattarulo, R., Pérez, J., Dendaluce, M.: A complete framework for developing and testing automated driving controllers. IFAC-PapersOnLine 50(1), 258–263 (2017)CrossRef
7.
OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees (2012). Armin Hornung; Kai M. Wurm; Maren Bennewitz; Cyrill Stachniss; Wolfram Burgard
8.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography: Thomas Collins; J.J. Collins; Donor Ryan (1981)
9.
Kumar, M., Hildebrandt, A.-C., Strauss, P., Kraus, S., Stiller, C., Zimmermann, A.: Lateral Trajectory Stabilization of an Articulated Truck during Reverse Driving Maneuvers. Ieee intelligent vehicles symposium (iv), Las Vegas, NV, USA 2020, 744–751 (2020). https://doi.org/10.1109/IV47402.2020.9304691
10.
Choi, J.-W., Huhtala, K.: Constrained global path optimization for articulated steering vehicles. IEEE Trans.Vehicular Technol. 65(4), 1868–1879 (2016). https://doi.org/10.1109/TVT.2015.2424933
11.
Saeki, M.: Path following control of articulated vehicle by backward driving. In: Proceedings of the International Conference on Control Applications, Glasgow, UK, 2002, pp. 421–426 vol. 1, https://doi.org/10.1109/CCA.2002.1040223
12.
Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Learning in centralized nonlinear model predictive control: application to an autonomous tractor-trailer system. IEEE Trans. Control Syst. Technol. 23(1), 197–205 (2015). https://doi.org/10.1109/TCST.2014.2321514CrossRef
