An Overtaking Obstacle Algorithm for Autonomous Driving based on Dynamic Trajectory Planning

In this paper, we present an integrated dynamic path planning and trajectory tracking control strategy for electric vertical take-off and landing (eVTOL) unmanned aerial vehicles (UAVs) by using a receding horizon optimization (RHO) approach. Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into minimum curvature-based RHO problems with a polynomial path template. By adding the contraction constraint derived from the backstepping controller, the proposed nonlinear model predictive control (NMPC), as an online optimization scheme, guarantees the closed-loop stability. It also achieves the optimal control performance by minimizing the tracking error and power consumption. The experimental results of the dynamic path planning and trajectory tracking demonstrate the effectiveness of the proposed control algorithm.

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

This paper discusses an autonomous driving control strategy based on Deep Reinforcement Learning (DRL), which aims to improve the decision-making ability of autonomous driving system in complex traffic environments. Deep reinforcement learning has a wide range of applications in many fields, such as robotics and medicine. Autonomous driving has emerged as a significant research focus in recent years. By combining deep learning and reinforcement learning, the model is able to autonomously learn and optimize driving behavior under dynamically changing road conditions. The DRL-based control strategy performs well in vehicle obstacle avoidance, pedestrian recognition, and traffic rule compliance in the face of complex environments such as city streets, intersections, and congested road sections, significantly improving the safety and efficiency of autonomous driving. This article will first introduce deep reinforcement learning. Then, the autonomous driving control strategy based on deep reinforcement learning is introduced. This research provides valuable insights for developing and implementing DRL-based autonomous driving systems.

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit-Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies-PD control, PD with gravity compensation, and the computed torque method-are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4-1.45 m/s, validating the effectiveness of the proposed model and control strategies.

As for a motion control framework of robots, virtual model control (VMC) can use virtual components to create virtual forces/torques. Actually, the virtual forces/torques are generated by joint actuators when the virtual components interact between robots and environments. In this paper, a virtual model control is proposed to do the dynamic balance control of quadruped robots in trot gait. In each leg, virtual model control includes swing phase control of robots and stance phase counterparts. In whole body, based on the forces/torques distribution method between two stance legs, virtual model control is mainly about the attitude control containing roll, pitch and yaw. Then, an intuitive approach of velocity control is employed for the locomotion of quadruped robots. Based on the velocity planning and control, a trajectory tracking control approach is investigated by considering four factors: terrain complexity index, curvature radius of given trajectory, distance to terminal, and maximum velocity of quadruped robots. Finally, the effectiveness of proposed controllers is validated by co-simulations.

SummaryIn this paper, we present an extended state observer–based robust dynamic surface trajectory tracking controller for a quadrotor unmanned aerial vehicle subject to parametric uncertainties and external disturbances. First, the original cascaded dynamics of a quadrotor unmanned aerial vehicle is formulated in a strict form with lumped disturbances to facilitate the backstepping design. Second, based on the separate outer‐ and inner‐loop control methodologies, the extended state observers are constructed to online estimate the unmeasurable velocity states and lumped disturbances existed in translational and rotational dynamics, respectively. Third, to overcome the problem of “explosion of complexity” inherent in backstepping control, the technique of dynamic surface control is utilized for trajectory tracking and attitude stabilization, and with the velocity and disturbance estimates incorporated into the dynamic surface control, a robust dynamic surface flight controller that guarantees asymptotic tracking in the presence of lumped disturbances is synthesized. In addition, the stability analysis is given, showing that the present robust controller can ensure the ultimate boundedness of all signals in the closed‐loop system and make the tracking errors arbitrarily small. Finally, comparisons and extensive simulations under different flight scenarios are performed to validate the effectiveness and superiority of the proposed scheme in accurate tracking performance and enhanced antidisturbance capability.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

Accurate dynamic modeling is difficult for aerobatic unmanned aerial vehicles flying at their physical limit, due to the model uncertainty caused by unobservable hidden states like airflow and vibrations. Although some progresses have been made, these hidden states are still not properly characterized, rendering system identification problem for aerobatic unmanned aerial vehicle extremely challenging. To address this issue, a novel spectrally normalized adaptive neural identifier is proposed for the dynamic modeling of aerobatic unmanned aerial vehicles. Specifically, to characterize the model uncertainty, we propose a spectrally normalized adaptive neural network (SNANet) to extract deep features representing the hidden states of the system. Particularly, the proposed SNANet adopts a multi-model adaptive structure, quickly and dynamically updating the model online. Furthermore, the spectral normalization constraint is introduced into the training process to ensure the Lipschitz stability of the SNANet. Consequently, a trajectory tracking control scheme including the sliding mode controller and SNANet is presented. The modeling effectiveness of the proposed method is verified on a real flight dataset. The results demonstrate that our method has high modeling accuracy, short training time, and fast model response speed.KeywordsSpectral normalizationModel uncertaintyAdaptive neural identifierAerobatic unmanned aerial vehicle

Dynamic trajectory planning study of planar two-dof redundantly actuated cable-suspended parallel robots

Dynamic path planning and trajectory tracking using MPC for satellite with collision avoidance

This paper describes a dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles (CAVs). The proposed dynamic lane-changing trajectory planning (DLTP) model adopts vehicle-to-vehicle (V2V) communication to generate an automated lane-changing maneuver with avoiding potential collisions and rollovers during the lane-changing process. The novelty of this method is that the DLTP model combines a detailed velocity planning strategy and considers more complete driving environment information. Besides, a lane-changing safety monitoring algorithm and a lane-changing starting-point determination algorithm are presented to guarantee the lane-changing safety, efficiency and stability of automated vehicles. Moreover, a trajectory-tracking controller based on model predictive control (MPC) is introduced to make the automated vehicle travel along the reference trajectory. The field traffic data from NGSIM are selected as the target dataset to simulate a real-world lane-changing driving environment. The simulations are performed in CarSim-Simulink platform and the experimental results show that the proposed method is effective for lane-changing maneuver.

<div class="section abstract"><div class="htmlview paragraph">Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral control scheme, double lane change and circumferential tests are designed to assess the comprehensive performance of the controller under medium-low adhesion road conditions. Additionally, the trajectory tracking performance of the proposed MPC controller is evaluated by comparing it with traditional Sliding Mode Control (SMC) and Linear Quadratic Regulator (LQR) trajectory tracking controllers. The simulation results indicate that the maximum heading error of the MPC controller is less than 0.09 rad, the heading error remains within 0.05 rad, and the front wheel angle stabilizes within 7.5 degrees under both operating conditions. Furthermore, the MPC controller ensures tracking accuracy and smoothness without any obvious jitter while minimizing control inputs to achieve optimal control, thereby significantly enhancing the accuracy and stability of the vehicle's trajectory tracking.</div></div>

This study constructs an integrated obstacle avoidance control strategy that can account for both lateral and longitudinal dynamic obstacles, enabling vehicles to quickly and stably evade obstacles in even emergency scenarios, thereby preventing accidents in complex road environments. Firstly, based on the safety distance model, a risk area dynamic partition method that considers both lateral and longitudinal directions is established. Subsequently, within the Frenet coordinate system, quintic polynomial candidate paths are generated based on the vehicle’s acceleration, speed, and position before and after lane-changing, and the optimal path is determined with consideration of driving safety and comfort, as well as the constraints associated with it. Finally, utilizing a two-degree-of-freedom vehicle dynamics model and a lateral tracking error model, a lateral tracking controller based on Linear Quadratic Regulator (LQR) and a longitudinal tracking controller based on double Proportional-Integral-Derivative (PID) approach are designed. The results of hardware-in-the-loop experiments demonstrate that, compared to the LQR algorithm, the proposed strategy more effectively ensures the stability and obstacle avoidance capability of the vehicle during operation. Furthermore, data collected under various scenarios indicate that the proposed strategy meets the requirements for tracking performance and driving comfort while successfully ensuring obstacle avoidance. Overall, the proposed method satisfies the criteria for safety, comfort, and stability in vehicle obstacle avoidance within complex environments.

A new track tracking control method based on Lagrange dynamic equation is proposed. Firstly, the kinetic energy equation and potential energy equation of the left and right robotic arms are derived and calculated based on Lagrange function and the robot dynamics equation in the generalized coordinate system. The friction model of the left and right robotic arms is designed based on the Stribeck model, and the Lagrangian dynamic equation of the dual-arm mechanism for the hatch docking is established. Secondly, based on the Lagrange dynamic model, the collaborative control system of the docking mechanism is designed, and the backstepping sliding mode controller is designed and analyzed. A fuzzy backstepping sliding mode controller is designed to control the gain matrix β of the backstepping sliding mode controller by adjusting the output of the system with fuzzy logic, which further improves the robustness of the control system. Finally, the effectiveness of the design, dynamic modeling and control algorithm design of the cabin docking mechanism is verified by software simulation and real experiment. The experimental results show that the control method can realize the accurate tracking and control of the motion trajectory and velocity trajectory of the dual-arm mechanism, and has good robustness. The maximum absolute error and standard deviation of the axis Angle of the mobile cabin after docking are 0.501° and 0.004°, which can meet the requirements of the actual production process.

Tracking fault-tolerant control based on model predictive control for human occupied vehicle in three-dimensional underwater workspace