scholarly journals A Model Based Motion Planning Framework for Automated Vehicles in Structured Environments

2019 ◽  
Author(s):  
Maximilian Graf ◽  
Oliver Speidel ◽  
Klaus Dietmayer
Keyword(s):  
Motion Planning ◽  
Automated Vehicles ◽  
Model Based ◽  
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.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

2022 ◽  
pp. 036119812110702
Author(s):  
Shunchao Wang ◽  
Zhibin Li ◽  
Bingtong Wang ◽  
Jingfeng Ma ◽  
Jingcai Yu
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Motion Tracking ◽  
Collision Risk ◽  
Automated Vehicles ◽  
Vehicle Collision ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Velocity Obstacle ◽  
Vehicle Dynamic Model

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

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.

Motion planning of a fish-like piezoelectric actuated robot using model-based predictive control

2021 ◽  
pp. 107754632110482
Author(s):  
Arthur S Barbosa ◽  
Lucas Z Tahara ◽  
Maíra M da Silva
Keyword(s):  
Motion Planning ◽  
Predictive Control ◽  
Fiber Composite ◽  
Beam Theory ◽  
Mode Shapes ◽  
Design Strategies ◽  
Mechanical Coupling ◽  
Model Based ◽  
Input Signals ◽  
Bounded Input

This work proposes a novel methodology for planning the motion of fish-like soft robots actuated by macro-fiber composite (MFC) pairs. These structures should mimic oscillatory and undulation movements, which can be accomplished if the amplitude of the tail motion is larger than that of the head motion. Design strategies, such as the use of concentrated and distributed masses, are addressed to mimic fish-like motion since they guarantee suitable mode shapes for the structure. The motion planning proposal explores a model-based predictive control (MPC) strategy for deriving the input signals for the MFC actuators. This model-based control strategy requires the use of reasonably small-sized models. This is accomplished by extracting modal state-space models based on the free–free Euler–Bernoulli beam theory considering the electro-mechanical coupling of the MFC actuator pairs. Numerical results demonstrate the capability of the proposal for deriving bounded input signals that generate oscillatory and undulation movements even in the presence of disturbances. This general approach can be further extended for other applications.

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