Herding by caging: a formation-based motion planning framework for guiding mobile agents

2021 ◽  
Author(s):  
Haoran Song ◽  
Anastasiia Varava ◽  
Oleksandr Kravchenko ◽  
Danica Kragic ◽  
Michael Yu Wang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Mobile Agents ◽  
Planning Framework

An interactive motion planning framework that can learn from experience

2015 ◽  
Vol 59 ◽  
pp. 23-38 ◽  
Author(s):  
Yu Yan ◽  
Emilie Poirson ◽  
Fouad Bennis
Keyword(s):  
Motion Planning ◽  
Planning Framework

scholarly journals Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics

2020 ◽  
Vol 10 (24) ◽  
pp. 9137
Author(s):  
Hongwen Zhang ◽  
Zhanxia Zhu
Keyword(s):  
Motion Planning ◽  
Inverse Kinematics ◽  
Random Trees ◽  
Space Robot ◽  
Initial Configuration ◽  
First Order ◽  
System A ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Differential Relationship

Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm.

A General Task and Motion Planning Framework For Multiple Manipulators

2021 ◽  
Author(s):  
Tianyang Pan ◽  
Andrew M. Wells ◽  
Rahul Shome ◽  
Lydia E. Kavraki
Keyword(s):  
Motion Planning ◽  
Planning Framework ◽  
Multiple Manipulators

Motion Planning Framework for Autonomous Vehicle with LSTM-based Predictive Model

2021 ◽  
Author(s):  
Xiaolin Tang ◽  
Guichuan Zhong ◽  
Kai Yang ◽  
Jiahang Wu ◽  
Zichun Wei
Keyword(s):  
Motion Planning ◽  
Predictive Model ◽  
Autonomous Vehicle ◽  
Planning Framework

scholarly journals A 3D motion planning framework for snake robots

2014 ◽  
Author(s):  
Pal Liljeback ◽  
Kristin Y. Pettersen ◽  
Oyvind Stavdahl ◽  
Jan Tommy Gravdahl
Keyword(s):  
Motion Planning ◽  
3D Motion ◽  
Planning Framework ◽  
Snake Robots

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

2011 ◽  
Vol 66 (4) ◽  
pp. 477-494 ◽  
Author(s):  
Andrew J. Berry ◽  
Jeremy Howitt ◽  
Da-Wei Gu ◽  
Ian Postlethwaite
Keyword(s):  
Motion Planning ◽  
Unmanned Vehicles ◽  
Complex Environments ◽  
Local Motion ◽  
Planning Framework

scholarly journals Coupling SUMO with a Motion Planning Framework for Automated Vehicles

10.29007/1p2d ◽  
2019 ◽  
Author(s):  
Moritz Klischat ◽  
Octav Dragoi ◽  
Mostafa Eissa ◽  
Matthias Althoff
Keyword(s):  
Motion Planning ◽  
Open Source ◽  
Dynamic Behavior ◽  
Real World ◽  
Software Framework ◽  
Automated Vehicles ◽  
Lane Changing ◽  
Realistic Simulation ◽  
Lateral Dynamics ◽  
Planning Framework

Testing motion planning algorithms for automated vehicles in realistic simulation environments accelerates their development compared to performing real-world test drives only. In this work, we combine the open-source microscopic traffic simulator SUMO with our software framework CommonRoad to test motion planning of automated vehicles. Since SUMO is not originally designed for simulating automated vehicles, we present an inter- face for exchanging the trajectories of vehicles controlled by a motion planner and the trajectories of other traffic participants between SUMO and CommonRoad. Furthermore, we ensure realistic dynamic behavior of other traffic participants by extending the lane changing model in SUMO to implement more realistic lateral dynamics. We demonstrate our SUMO interface with a highway scenario.

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