Hongchao Zhao

This brief presents real-time configuration estimation and motion planning for the industrial tractor–trailers vehicle composed of a full-scale car-like tractor and multiple full trailers. For the real-life vehicle, determining the configuration is challenging. With only on-tractor sensors, we solve this problem by fusing information from system dynamics propagation, geometrical constraints among the articulated units, and matching with the prebuilt environment map. The solution is efficiently achieved by formulating and solving a maximum a posterior (MAP) estimation problem in the pose-graph optimization framework. With the complicated tractor–trailers’ structure, small mismatch between actual and planned trajectories is crucial to inherit obstacle-free guarantee from planning to execution. We consider the dynamics and focus on facilitating the reduction of the mismatch by proposing a controller-based smoothing method to perform online motion planning. The given waypoint path is smoothed by forward propagation using a deliberately designed controller to generate the trajectory. The efficiently computed trajectories are also obstacle-free and dynamically feasible. The controller is also applied in execution to precisely reproduce the planned system evolvement. We demonstrate the performance with the real-life industrial tractor–trailers’ vehicle.

Most of the previously proposed vehicle controllers are developed with the no-skid assumption, also presume that full states of the vehicle are available, which are usually violated for industrial tractors. This article dedicates to building a practical vision-based antiskid tracking control framework for industrial tractors. For that, we first present a novel nonlinear visual–inertial estimator (VIE) to estimate the real-time position and velocity of the tractor in unstructured environments (GPS-denied, no prior feature maps, etc.). Subsequently, VIE-based kinematic and dynamic controllers are designed for trajectory tracking. The proposed kinematic controller is easy to implement. It works well even if the vehicle is towing payloads since the sideslip angle can be reactively estimated and compensated. Besides, by fully considering vehicle dynamics, the VIE-based dynamic controller can be compatible with occasions that require higher control performance. It is proved by Lyapunov theory that the asymptotic stability of the closed-loop estimation-control system is guaranteed. Extensive full-scale experiments on an autonomous tractor are implemented to show the validity of the VIE-based control system in unstructured environments.

This letter presents a practical method for parameter estimation of a full-size industrial car-like tractor. With only on-vehicle sensors including three encoders, an IMU and a 3D LiDAR, we estimate the effective radius of rear wheels, ratio between the steering wheel angle and road wheel angle, the longitudinal position of center of gravity (COG), the yaw moment of inertia and the longitudinal and cornering tire stiffnesses. This letter innovatively introduces nonlinear vehicle-dynamics constraints into a factor-graph estimation framework that also fuses IMU and LiDAR measurements, thus achieving an easy-to-tune and highly-accurate parameter estimation solution. The batch maximum a posterior (MAP) problem is formulated and solved efficiently for both states and parameters. To make it even easier to tune, we perform specifically-designed motion patterns to simplify the required description model, and we estimate as fewer parameters as possible for one motion pattern. The dynamic constraints are also formulated according to the motion properties. Performance and results of the proposed method are validated in detail with experiments on the industrial car-like tractor.

This article presents the dynamic modeling and trajectory tracking control of the industrial tractor-trailers vehicle composed of a full-size car-like tractor and multiple full trailers. To eliminate the high uncertainty of the kinematic and dynamic parameters of the trailers in the model, we propose to install a force sensor to monitor the forces exerted on the tractor in real time. With the force measurements, the tractor can be considered separately and the dynamic model is significantly simplified. The inaccurate parameters and varying configurations of the trailers can all be ignored, whereas their dynamic effects on the tractor are still precisely captured. A Lyapunov-based trajectory tracking controller is proposed to online compensate the forces and drive the tractor onto the desired trajectory. The controller is developed using backstepping techniques and the stability is rigorously proved. The performance is illustrated with practical implementations on a full-scale industrial tractor towing several full trailers. The robustness and accuracy of the controller with force compensation are verified with different trailer settings. Implementation results show that applying the simplified model has generally superior performance than using the whole-vehicle model.

Automated driving of a heavy tractor-trailers vehicle using only low-cost onboard sensors is challenging. Complex system dynamics and towing configuration changes degrade the performance of conventional model-based estimation and control methods. In this paper, we propose an onboard-sensor-based automatic control system for a heavy tractor-trailers vehicle used in low-speed industrial applications. First, by introducing a new hitch force sensor, a tractor dynamic state estimator (TDSE) is proposed. It integrates the kinematic and dynamic information sources such that accurate and reactive dynamic states and model parameters of the lead tractor can be obtained simultaneously. Second, building on top of the TDSE, a highly practical integrated dynamic controller (IDC) is developed. It reliably enforces the heavy tractor-trailers vehicle to track the reference path and velocity under any towing situation. The performance of the proposed TDSE and IDC is proved theoretically. TDSE and IDC are also integrated into a full-size tractor-trailers vehicle, and their feasibility and superiority are validated through extensive experiments.

This paper aims to build a general path tracking framework for heavy industrial tractor-trailers systems in GPS-denied environments. Compared to existing methods, this approach prioritizes a robust, cost-optimized and easy-to-implement solution for large-scale industrial applications. First, to achieve accurate path tracking, a force sensor is subtly introduced to capture the impact of an arbitrary number of trailers and varying payloads on the tractor dynamics. A robust controller is then designed on the basis of a newly derived tractor dynamic model and the lateral force compensation. Second, a novel visual-inertial estimator, which explicitly considers the nonlinear system dynamics, is developed to allow real-time translational velocity and position estimation for dynamic feedback control. It is rigorously proved by the Lyapunov theory that the stability of the proposed estimation and control system is guaranteed. Extensive full-scale experiments are conducted to demonstrate the feasibility of the approach.

Existing works on control of tractor-trailers systems only consider the kinematics model without taking dynamics into account. Also, most of them treat the issue as a pure control theory problem whose solutions are difficult to implement. This paper presents a trajectory tracking control approach for a full-scale industrial tractor-trailers vehicle composed of a car-like tractor and arbitrary number of passive full trailers. To deal with dynamic effects of trailing units, a force sensor is innovatively installed at the connection between the tractor and the first trailer to measure the forces acting on the tractor. The tractor's dynamic model that explicitly accounts for the measured forces is derived. A tracking controller that compensates the pulling/pushing forces in real time and simultaneously drives the system onto desired trajectories is proposed. The propulsion map between throttle opening and the propulsion force is proposed to be modeled with a fifth-order polynomial. The parameters are estimated by fitting experimental data, in order to provide accurate driving force. Stability of the control algorithm is rigorously proved by Lyapunov methods. Experiments of full-size vehicles are conducted to validate the performance of the control approach.