Pseudospectral motion planning techniques for autonomous obstacle avoidance

2007 ◽  
Author(s):  
L. Ryan Lewis ◽  
I. Michael Ross ◽  
Qi Gong
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Planning Techniques

scholarly journals An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

2021 ◽  
Vol 10 (3) ◽  
pp. 687
Author(s):  
Naitik Nakrani ◽  
Maulin M. Joshi
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Intelligent Design ◽  
Autonomous Vehicle ◽  
Planning Techniques ◽  
Vehicle Localization ◽  
Adaptive Motion ◽  
Planning Algorithm

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

Trajectory Planning and Obstacle Avoidance for Hyper-Redundant Serial Robots

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
Midhun S. Menon ◽  
V. C. Ravi ◽  
Ashitava Ghosal
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Medical Robotics ◽  
Theoretical Development ◽  
Wheeled Mobile Robots ◽  
Calculus Of Variation ◽  
Redundant Robot ◽  
Cluttered Environments ◽  
Serial Robots ◽  
Actuated Joints

Hyper-redundant snakelike serial robots are of great interest due to their application in search and rescue during disaster relief in highly cluttered environments and recently in the field of medical robotics. A key feature of these robots is the presence of a large number of redundant actuated joints and the associated well-known challenge of motion planning. This problem is even more acute in the presence of obstacles. Obstacle avoidance for point bodies, nonredundant serial robots with a few links and joints, and wheeled mobile robots has been extensively studied, and several mature implementations are available. However, obstacle avoidance for hyper-redundant snakelike robots and other extended articulated bodies is less studied and is still evolving. This paper presents a novel optimization algorithm, derived using calculus of variation, for the motion planning of a hyper-redundant robot where the motion of one end (head) is an arbitrary desired path. The algorithm computes the motion of all the joints in the hyper-redundant robot in a way such that all its links avoid all obstacles present in the environment. The algorithm is purely geometric in nature, and it is shown that the motion in free space and in the vicinity of obstacles appears to be more natural. The paper presents the general theoretical development and numerical simulations results. It also presents validating results from experiments with a 12-degree-of-freedom (DOF) planar hyper-redundant robot moving in a known obstacle field.

Motion planning problem with obstacle avoidance for step-2 bracket-generating systems

PAMM ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 799-800 ◽  
Author(s):  
Victoria Grushkovskaya ◽  
Alexander Zuyev
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Planning Problem ◽  
Motion Planning Problem

Motion Planning and Obstacle Avoidance

2016 ◽  
pp. 1177-1202 ◽  
Author(s):  
Javier Minguez ◽  
Florant Lamiraux ◽  
Jean-Paul Laumond
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

Robotica ◽  
2009 ◽  
Vol 28 (6) ◽  
pp. 833-846 ◽  
Author(s):  
Yuan Mingxin ◽  
Wang Sun'an ◽  
Wu Canyang ◽  
Li Kunpeng
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Immune Network ◽  
Network Algorithm ◽  
Dynamic Obstacle Avoidance ◽  
Optimal Dynamic ◽  
Dynamic Obstacle

SUMMARYInspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

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