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.

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.

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.

scholarly journals Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning

2018 ◽  
Author(s):  
Fahad Islam ◽  
Oren Salzman ◽  
Maxim Likhachev
Keyword(s):  
Motion Planning ◽  
Domain Knowledge ◽  
High Dimensional ◽  
Constrained Motion ◽  
Significant Progress ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Speed Up ◽  
User Guidance ◽  
Intermediate Configuration

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration q^, which is used to bias the planner to go through q^. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafted domain-dependent heuristics.

scholarly journals A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Y. J. Zhang ◽  
F. Du ◽  
J. Wang ◽  
L. S. Ke ◽  
M. Wang ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Vehicle Safety ◽  
Model Parameters ◽  
Collision Risk ◽  
Environmental Characteristics ◽  
Time Model ◽  
System A ◽  
Vehicle Collision ◽  
Collision Avoidance System ◽  
The Relationship

Aiming at the requirements of vehicle safety collision avoidance system, a safety collision avoidance algorithm based on environmental characteristics and driver characteristics is proposed. By analyzing the relationship between collision avoidance time and the environment, a safety time model is established. In the established safety time model, parameters based on driver characteristics are added, which increases the flexibility of the algorithm. The algorithm can adapt to more different driving conditions and give appropriate warning thresholds. After simulation and comparison with other algorithms, the algorithm proposed in this paper can satisfied the requirements of reducing vehicle collision risk. The effectiveness and feasibility of the algorithm are verified, and the safety of vehicle driving can be improved.

On-Site Locomotion Planning for a Humanoid Robot with Stable Whole-Body Collision Avoidance Motion Guided by Footsteps and Centroidal Trajectory

2019 ◽  
Vol 17 (01) ◽  
pp. 1950035
Author(s):  
Iori Kumagai ◽  
Mitsuharu Morisawa ◽  
Shin’ichiro Nakaoka ◽  
Fumio Kanehiro
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Dynamic Simulation ◽  
Inverse Kinematics ◽  
Humanoid Robot ◽  
Computational Cost ◽  
Body Motion ◽  
Whole Body ◽  
Planning Framework ◽  
Narrow Space

In this paper, we propose a locomotion planning framework for a humanoid robot with stable whole-body collision avoidance motion, which enables the robot to traverse an unknown narrow space on the spot based on environmental measurements. The key idea of the proposed method is to reduce a large computational cost for the whole-body locomotion planning by utilizing global footstep planning results and its centroidal trajectory as a guide. In the global footstep planning phase, we modify the bounding box of the robot approximating the centroidal sway amplitude of the candidate footsteps. This enables the planner to obtain appropriate footsteps and transition time for next whole-body motion planning. Then, we execute sequential whole-body motion planning by prioritized inverse kinematics considering collision avoidance and maintaining its ZMP trajectory, which enables the robot to plan stable motion for each step in 223[Formula: see text]ms at worst. We evaluated the proposed framework by a humanoid robot HRP-5P in the dynamic simulation and the real world. The major contribution of our paper is solving the problem of increasing computational cost for whole-body motion planning and enabling a humanoid robot to execute adaptive on-site locomotion planning in an unknown narrow space.

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler

2020 ◽  
Vol 14 (10) ◽  
pp. 1200-1209
Author(s):  
Nurbaiti Wahid ◽  
Hairi Zamzuri ◽  
Noor H. Amer ◽  
Abdurahman Dwijotomo ◽  
Sarah Atifah Saruchi ◽  
...  
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Planning Strategy ◽  
Vehicle Collision

scholarly journals Generation and Selection of Driver-behavior-based Transferable Motion Primitives

2020 ◽  
Author(s):  
Haijie Guan ◽  
Boyang Wang ◽  
Jiaming Wei ◽  
Yaomin Lu ◽  
Huiyan Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Driver Behavior ◽  
Single Step ◽  
Driving Experience ◽  
Motion Primitives ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Motion Characteristics ◽  
Selection Of

Abstract In order to achieve the integration of driver experience and heterogeneous vehicle platform characteristics in the motion planning algorithm, based on the driver-behavior-based transferable motion primitives, a general motion planning framework for oine generation and online selection of motion primitives (MPs) is proposed. The optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, this paper proposes a layered, unequal-weighted MPs selection framework and utilizes the combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated oine demonstrates that the proposed generation method realizes the eective expansion of the MP types and achieves the diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes the unique MP library to achieve the online extension of MP sequences. The results show that the proposed motion planning framework can not only improve the eciency and rationality of the algorithm based on driving experience but also can transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

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