Trajectory optimization of autonomous driving by differential dynamic programming

Huang, Weiwei; Wu, Xiaojun; Zhang, Qun; Wu, Ning; Song, Zhiwei

doi:10.1109/icarcv.2014.7064582

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…It was found that the best solution obtained was in the vicinity of a local optimal path. Huang et al (2014), presented a realtime optimization method for AV motion planning using differential dynamic programming. Motion planning includes trajectory generation and trajectory optimization.…”

Section: Path Planningmentioning

confidence: 99%

“…Motion planning includes trajectory generation and trajectory optimization. Conventional methods of optimization involve trajectory smoothing however, Huang et al (2014), decided to integrate differential dynamic programming to solve the optimization problem. The motion planner was tested for obstacle avoidance, however, as this was not the focus of the study the algorithm was not modified with that in mind.…”

Section: Path Planningmentioning

confidence: 99%

See 1 more Smart Citation

Motion Boundaries Formation For Autonomous Vehicles

Bannis¹

2021

Preprint

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<div>In this paper, the application of Model Predictive Control to perform curvilinear motion planning is explored. More specifically, nonlinear MPC will be focused on because of its proven efficiency in the modeling of uncertainties as well as in nonlinear model dynamics. The main objective of this report is to show that with proper modeling and formulation of motion constraints, curvilinear motion planning can be achieved with nonlinear MPC. The trajectory of the vehicle will be tracked with the least error while satisfying constraints such as speed and steering angles. Simulations are presented which demonstrate the ability of the suggested models to successfully perform curvilinear motion staying safely within the bounds, while simulations of several models validate its performance. A deterministic sensitivity analysis was conducted in order to determine the impact</div><div>of the prediction horizon time. Experimental results show that a critical prediction horizon time approximately 10 to 13 seconds was identified as the ideal range for optimal results of the model.</div>

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Section: Path Planningmentioning

confidence: 99%

Section: Path Planningmentioning

confidence: 99%

Motion Boundaries Formation For Autonomous Vehicles

Bannis¹

2021

Preprint

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“…The differential drive motor control technique has been taken from learning robotics using python as presented in [20]. Paper [21] explains the motion planning of the robot using the differential drive and trajectory smoothing using optimization techniques.Mathematical modeling and behavior of the robot are explained using the skid steering model, the tracking of the robot is also mentioned in paper [22].…”

Section: Related Workmentioning

confidence: 99%

Angular Orientation of Steering Wheel for Differential Drive

Megalingam¹,

Nagalla²,

Pasumarthi³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Several drive mechanisms for different robots are at hand in current days. Bicycle steering, Ackerman steering, differential drive are some principal drive mechanisms that are being deployed in robots these days. The differential drive needs the wheel rotations to be updated very frequently. But it is most commonly deployed on the robots with two wheels and casters, as discussed in this work. It also can be used to have an independent control for each of the wheels with independent control signals. This work deals with the modeling of the differential drive mechanism for a robot with two main drive wheels and two casters, which takes the angular orientation of the steering wheel as input. For simplicity, this work considers that casters have no influence on any aspect of the differential drive. An adaptive model, whose output depends on the real-time input from the gamers steering wheel and produces required output has been formulated. This work differs from the other differential drives in the context that the steering wheel and the robot wheels have no physical connection. The proposed model has been implemented in python and integrated with the Robot Operating System (ROS). The steering wheel, which is used to generate commands using, is attached to the controller at the control station and the ROS_Node thus created is used to read the values from the steering and generate commands for each of the left and right wheels. These commands are transferred to the controller on the mobile platform, which in turn generates control signals for actuators. This work also deals with the deployment of the proposed model using the Universal Robot Description Format (URDF) of the robot in the Gazebo simulation and evaluating it using the Nitho Drive Pro steering wheel. To prove that the differential drive mechanism can be used to control the robot efficiently in any type of terrain, a ROS python node is used to control and maneuver the robot.

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“…This model, as well as similar models, builds a trajectory over time in the form of a differential equation, where it is beneficial to have a small state space describing each point of a trajectory, since the required computational time and space scales with the order n, possibly exponentially (cf. [6], [16], [22]).…”

Section: Introductionmentioning

confidence: 99%

Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning

Ziehn

Ruf

Roschani

et al. 2020

2020 5th International Conference on Robotics and Automation Engineering (ICRAE)

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Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality criteria such as safety, comfort or ecology. Depending on the computation process, models with different assumptions and levels of detail are used; since maneuver planning usually has strong requirements for computation speed at a potentially high number of trajectory evaluations per planning cycle, most of the applied models aim to reduce complexity by implicitly or explicitly introducing simplifying assumptions. While evaluations show that these assumptions may be sufficiently valid under typical conditions, their effect has yet to be studied conclusively.We propose a model for vehicle dynamics that is convenient for maneuver planning by supporting both an analytic approach of extracting parameters from a given trajectory, and a generative approach of establishing a trajectory from given control inputs. Both applications of the model are evaluated in real-world test drives under dynamic conditions, both on a closed-off test track and on public roads, and effects arising from the simplifying assumptions are analyzed.

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Trajectory optimization of autonomous driving by differential dynamic programming

Cited by 5 publications

References 11 publications

Motion Boundaries Formation For Autonomous Vehicles

Motion Boundaries Formation For Autonomous Vehicles

Angular Orientation of Steering Wheel for Differential Drive

Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning

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