scholarly journals Motion Planning in Complex Environments Using Closed-loop Prediction

2008 ◽  
Author(s):  
Yoshiaki Kuwata ◽  
Justin Teo ◽  
Sertac Karaman ◽  
Gaston Fiore ◽  
Emilio Frazzoli ◽  
...  
Keyword(s):  
Motion Planning ◽  
Closed Loop ◽  
Complex Environments ◽  
Loop Prediction

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

A Dataset Generation Tool for Deep learning-based Motion Planning in Complex Environments

2021 ◽  
Author(s):  
Muhammad Usman Sarwar ◽  
Moman Sohail ◽  
Muhayy Ud Din ◽  
Jan Rosell ◽  
Wajahat M Qazi
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Complex Environments

Curvature Aware Motion Planning with Closed-Loop Rapidly-exploring Random Trees

2021 ◽  
Author(s):  
Berend van den Berg ◽  
Bruno Brito ◽  
Mohsen Alirezaei ◽  
Javier Alonso-Mora
Keyword(s):  
Motion Planning ◽  
Closed Loop ◽  
Random Trees ◽  
Rapidly Exploring Random Trees

Efficient Human Motion Planning Using Recursive Dynamics and Optimal Control Techniques

1999 ◽  
Author(s):  
Janzen Lo ◽  
Dimitris Metaxas
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Closed Loop ◽  
High Efficiency ◽  
Human Motion ◽  
Lagrangian Formulation ◽  
Planning Problem ◽  
Tree Structures ◽  
Recursive Dynamics ◽  
Minimum Torque

Abstract We present an efficient optimal control based approach to simulate dynamically correct human movements. We model virtual humans as a kinematic chain consisting of serial, closed-loop, and tree-structures. To overcome the complexity limitations of the classical Lagrangian formulation and to include knowledge from biomechanical studies, we have developed a minimum-torque motion planning method. This new method is based on the use of optimal control theory within a recursive dynamics framework. Our dynamic motion planning methodology achieves high efficiency regardless of the figure topology. As opposed to a Lagrangian formulation, it obviates the need for the reformulation of the dynamic equations for different structured articulated figures. We use a quasi-Newton method based nonlinear programming technique to solve our minimum torque-based human motion planning problem. This method achieves superlinear convergence. We use the screw theoretical method to compute analytically the necessary gradient of the motion and force. This provides a better conditioned optimization computation and allows the robust and efficient implementation of our method. Cubic spline functions have been used to make the search space for an optimal solution finite. We demonstrate the efficacy of our proposed method based on a variety of human motion tasks involving open and closed loop kinematic chains. Our models are built using parameters chosen from an anthropomorphic database. The results demonstrate that our approach generates natural looking and physically correct human motions.

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 Multitarget Search of Swarm Robots in Unknown Complex Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
You Zhou ◽  
Anhua Chen ◽  
Hongqiang Zhang ◽  
Xin Zhang ◽  
Shaowu Zhou
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Force Model ◽  
Complex Environments ◽  
Target Response ◽  
Complex Environment ◽  
Multiple Targets ◽  
Virtual Force ◽  
Swarm Robots ◽  
Division Model

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.

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