scholarly journals Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020 ◽  
Vol 10 (21) ◽  
pp. 7716
Author(s):  
Tamás Hegedűs ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Low Complexity ◽  
The Real ◽  
Time Motion ◽  
Planning Algorithm ◽  
Vehicle Motion ◽  
Vehicle Trajectory

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

scholarly journals Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling

2020 ◽  
Vol 10 (9) ◽  
pp. 3180 ◽  
Author(s):  
Dongfang Dang ◽  
Feng Gao ◽  
Qiuxia Hu
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamical Model ◽  
Planning Problem ◽  
Original Design ◽  
Automatic Driving ◽  
Wide Range ◽  
Planning Algorithm ◽  
Dynamical Coupling

Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.

scholarly journals Real-Time Motion Planning for Agile Autonomous Vehicles

2002 ◽  
Vol 25 (1) ◽  
pp. 116-129 ◽  
Author(s):  
Emilio Frazzoli ◽  
Munther A. Dahleh ◽  
Eric Feron
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Time Motion

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1327-1335 ◽  
Author(s):  
Nir Shvalb ◽  
Boaz Ben Moshe ◽  
Oded Medina
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Morse Function ◽  
Climbing Robot ◽  
Probabilistic Algorithm ◽  
Road Map ◽  
Time Motion ◽  
Planning Algorithm

SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

Construction Research on Multi-Threshold Segmentation Based on Improved Otsu Threshold Method

2014 ◽  
Vol 1046 ◽  
pp. 425-428 ◽  
Author(s):  
Yan Qing Wang ◽  
Lu Lu Zhuang ◽  
Chao Xia Shi
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Intelligent Vehicle ◽  
Road Detection ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
The Real ◽  
Time Motion ◽  
Double Threshold

The aim of unstructured road detection is the recognition of road region and the tracking of road boundary. An improved Otsu threshold method available in double-threshold segmentation was proposed in this paper to resolve the multi-peaks problem in both complicated background and target. This method remedies the mis-segmentation of Otsu threshold method caused by huge diversity of between-class variances and refines the segmentation precision by matching the proposed Otsu edges with the weighted Canny edges. According to the irregular characteristics of the unstructured road and the requirement of the real-time motion planning for intelligent vehicle, improved Otsu threshold method was proposed. The experiment results in different road environments demonstrated the validity and real-time of the proposed method.

scholarly journals An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

2021 ◽  
Vol 10 (3) ◽  
pp. 687
Author(s):  
Naitik Nakrani ◽  
Maulin M. Joshi
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Intelligent Design ◽  
Autonomous Vehicle ◽  
Planning Techniques ◽  
Vehicle Localization ◽  
Adaptive Motion ◽  
Planning Algorithm

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

scholarly journals Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot

2021 ◽  
Vol 11 (23) ◽  
pp. 11336
Author(s):  
Zuoliang Tang ◽  
Lijia Xu ◽  
Yuchao Wang ◽  
Zhiliang Kang ◽  
Hong Xie
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Jacobian Matrix ◽  
Joint Space ◽  
Artificial Potential Field ◽  
The Novel ◽  
Native Defects ◽  
The Real ◽  
Time Performance ◽  
Planning Algorithm

This paper presents the results of a motion planning algorithm that has been used in an intelligent citrus-picking robot consisting of a six-link manipulator. The real-time performance of a motion planning algorithm is urgently required by the picking robot. Within the artificial potential field (APF) method, the motion planning of the picking manipulator was basically solved. However, the real-time requirement of the picking robot had not been totally satisfied by APF because of some native defects, such as the large number of calculations used to map forces into torques by the Jacobian matrix, local minimum trap, and target not reachable problem, which greatly reduce motion planning efficiency and real-time performance of citrus-picking robots. To circumvent those problems, this paper proposed some novel methods that improved the mathematical models of APF and directly calculates the attractive torques in the joint space. By using the latter approach, the calculation time and the total joint error were separately reduced by 54.89% and 45.41% compared with APF. Finally, the novel algorithm is presented and demonstrated with some illustrative examples of the citrus picking robot, both offline during the design phase as well as online during a realistic picking test.

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