scholarly journals Dynamically Feasible Probabilistic Motion Planning in Complex Environments for UAVs

10.5772/7322 ◽  
2010 ◽  
Author(s):  
Emre Koyuncu ◽  
Gokhan Inalh
Keyword(s):  
Motion Planning ◽  
Complex Environments

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

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

Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

2015 ◽  
Vol 776 ◽  
pp. 396-402 ◽  
Author(s):  
Nukman Habib ◽  
Adi Soeprijanto ◽  
Djoko Purwanto ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Initial Position ◽  
Ant Colony ◽  
Robot Motion ◽  
Complex Environments ◽  
Point To Point ◽  
Global Planning

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

Clearance-driven motion planning for mobile robots with differential constraints

Robotica ◽  
2018 ◽  
Vol 36 (7) ◽  
pp. 971-993 ◽  
Author(s):  
Evis Plaku ◽  
Erion Plaku ◽  
Patricio Simari
Keyword(s):  
Motion Planning ◽  
Medial Axis ◽  
Weighted Graph ◽  
Minimum Cost ◽  
Explicit Construction ◽  
Complex Environments ◽  
Differential Constraints ◽  
Cost Paths ◽  
Triangular Tessellation ◽  
High Clearance

SUMMARYThis paper presents an approach that integrates the geometric notion of clearance (distance to the closest obstacle) into sampling-based motion planning to enable a robot to safely navigate in challenging environments. To reach the goal destination, the robot must obey geometric and differential constraints that arise from the underlying motion dynamics and the characteristics of the environment. To produce safe paths, the proposed approach expands a motion tree of collision-free and dynamically feasible motions while maintaining locally maximal clearance. In distinction from related work, rather than explicitly constructing the medial axis, the proposed approach imposes a grid or a triangular tessellation over the free space and uses the clearance information to construct a weighted graph where edges that connect regions with low clearance have high cost. Minimum-cost paths over this graph produce high-clearance routes that tend to follow the medial axis without requiring its explicit construction. A key aspect of the proposed approach is a route-following component which efficiently expands the motion tree to closely follow such high-clearance routes. When expansion along the current route becomes difficult, edges in the tessellation are penalized in order to promote motion-tree expansions along alternative high-clearance routes to the goal. Experiments using vehicle models with second-order dynamics demonstrate that the robot is able to successfully navigate in complex environments. Comparisons to the state-of-the-art show computational speedups of one or more orders of magnitude.

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
Sign in / Sign up

Export Citation Format

Share Document