scholarly journals An Adaptive Motion Planning Technique for On-Road Autonomous Driving

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 2655-2664
Author(s):  
Xianjian Jin ◽  
Zeyuan Yan ◽  
Guodong Yin ◽  
Shaohua Li ◽  
Chongfeng Wei
Keyword(s):  
Motion Planning ◽  
Autonomous Driving ◽  
Adaptive Motion ◽  
Planning Technique

Adaptive motion planning framework by learning from demonstration

2019 ◽  
Vol 46 (4) ◽  
pp. 541-552 ◽  
Author(s):  
Xiao Li ◽  
Hongtai Cheng ◽  
Xiaoxiao Liang
Keyword(s):  
Motion Planning ◽  
Probabilistic Model ◽  
Training Data ◽  
Data Driven ◽  
Learning From Demonstration ◽  
Traditional Methods ◽  
Content Type ◽  
Adaptive Motion ◽  
Unstructured Environment ◽  
Planning Technique

Purpose Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique. Design/methodology/approach The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates. Findings The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully. Originality/value Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

Local Motion Planning and Tracking of Autonomous Driving Vehicles*

2020 ◽  
Author(s):  
Wangwang Zhu ◽  
Xi Zhang ◽  
Baixuan Zhao ◽  
Shiwei Peng ◽  
Pengfei Guo ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Driving ◽  
Local Motion

Adaptive motion selection for online hand–eye calibration

Robotica ◽  
2007 ◽  
Vol 25 (5) ◽  
pp. 529-536
Author(s):  
Jing Zhang ◽  
Fanhuai Shi ◽  
Yuncai Liu
Keyword(s):  
Motion Planning ◽  
Dynamic Threshold ◽  
Selection Algorithm ◽  
Robot System ◽  
End Effector ◽  
Threshold Determination ◽  
Robot Gripper ◽  
Adaptive Motion ◽  
Selection For ◽  
Relative Pose

SUMMARYWhile a robot moves, online hand–eye calibration to determine the relative pose between the robot gripper/end-effector and the sensors mounted on it is very important in a vision-guided robot system. During online hand–eye calibration, it is impossible to perform motion planning to avoid degenerate motions and small rotations, which may lead to unreliable calibration results. This paper proposes an adaptive motion selection algorithm for online hand–eye calibration, featured by dynamic threshold determination for motion selection and getting reliable hand–eye calibration results. Simulation and real experiments demonstrate the effectiveness of our method.

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