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.

scholarly journals Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives

2021 ◽  
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 offline 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 offline demonstrates that the proposed generation method realizes the effective 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 efficiency 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.

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.

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 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 E3MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Computational Efficiency ◽  
Path Optimization ◽  
System Structure ◽  
Optimization Approach ◽  
Complex Environments ◽  
Planning Stage ◽  
Motion Primitives ◽  
Planning Framework ◽  
Local Path

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21\% in the global planning stage and the motion efficiency of the robot is improved by 22.87\% compared with the recent quintic B\'{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathbf{^3} $MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.

Semi-stratified Motion Planning of Multi-agent Manipulation

2001 ◽  
Vol 5 (5) ◽  
pp. 248-256
Author(s):  
István Harmati ◽  
◽  
Ma Lantos ◽  
Shahram Payandeh ◽  
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Kinematic Model ◽  
Reference Points ◽  
Actual State ◽  
Smooth Motion ◽  
Planning Algorithm ◽  
Multi Agent ◽  
Frame Work ◽  
Selection Of

The paper addresses a manipulation problem based on kinematic model where the equations of the motion of the system can change discontinuously depending on the actual state of the system. In this case, the configuration space is stratified. The stratified control theory using the smooth motion planning algorithm promises a powerful alternative for this manipulation problem, however, it meets also some significant difficulties. The paper presents the main steps of developing computational frame work and some improvements for stratified motion planning. The concept proposes a semi-stratified method which decomposes the manipulation problem into stratified motion planning and pure finger relocation through the special selection of the reference points. Because of the different type of computational tasks (symbolic, numerical), the realization steps are also investigated.

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

scholarly journals A new method on motion planning for mobile robots using jump point search and Bezier curves

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110192
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Motion Planning ◽  
Initial Point ◽  
Target Point ◽  
Jump Point ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Front End ◽  
Planning Algorithm ◽  
Point Search ◽  
Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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