scholarly journals Motion representations for the Lafferriere-Sussmann algorithm for nilpotent control systems

2011 ◽  
Vol 21 (3) ◽  
pp. 525-534
Author(s):  
Ignacy Dulęba ◽  
Jacek Jagodziñski
Keyword(s):  
Motion Planning ◽  
Control Systems ◽  
Configuration Space ◽  
Cluttered Environment ◽  
Final Configuration ◽  
Planning Task

Motion representations for the Lafferriere-Sussmann algorithm for nilpotent control systems In this paper, an extension of the Lafferriere-Sussmann algorithm of motion planning for driftless nilpotent control systems is analyzed. It is aimed at making more numerous admissible representations of motion in the algorithm. The representations allow designing a shape of trajectories joining the initial and final configuration of the motion planning task. This feature is especially important in motion planning in a cluttered environment. Some natural functions are introduced to measure the shape of a trajectory in the configuration space and to evaluate trajectories corresponding to different representations of motion.

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.

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

2018 ◽  
Vol 94 (1) ◽  
pp. 265-282 ◽  
Author(s):  
Somaiyeh MahmoudZadeh ◽  
David M. W. Powers ◽  
Karl Sammut ◽  
Amir Mehdi Yazdani ◽  
Adham Atyabi
Keyword(s):  
Motion Planning ◽  
Task Allocation ◽  
Allocation Model ◽  
Planning Task ◽  
Ocean Environment ◽  
Hybrid Motion

Solving the motion planning problem by using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (4) ◽  
pp. 323-333 ◽  
Author(s):  
R.H.T. Chan ◽  
P.K.S. Tam ◽  
D.N.K. Leung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Configuration Space ◽  
Logic Gates ◽  
Moving Object ◽  
Planning Problem ◽  
Processing Unit ◽  
Configuration Point ◽  
Motion Planning Problem

SUMMARYThis paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

Probabilistic Roadmaps Based Motion Planning of a 4 Degree of Freedom Robot

2012 ◽  
Vol 241-244 ◽  
pp. 1922-1930
Author(s):  
Yu Tian Liu
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Shortest Path ◽  
Degrees Of Freedom ◽  
Learning Phase ◽  
Degree Of Freedom ◽  
Motion Path ◽  
Probabilistic Roadmaps ◽  
Two Phases ◽  
4 Degrees Of Freedom

In this paper, we used a probabilistic roadmaps(PRM) method to plan a motion path for a 4 degrees of freedom(DOF) robot in static workspace. This methods includes two phases: a learning phase and a query phase. In learning phase, a roadmap is constructed and stored as a graph , in which stores all of the random collision-free configurations in free configuration space denoted by and keeps all of the edges corresponding to feasible paths between these configurations. In query phase, the algorithm tries to connect any given initial and goal configuration to the nodes in the graph. And then the Dijkstra's algorithm searches for a shortest path to concatenate these two nodes. The experiment result demonstrates that this method applying to this 4 degrees of freedom robot works well.

Tool path optimization for robotic surface machining by using sampling-based motion planning algorithms

2020 ◽  
pp. 1-28
Author(s):  
Lu Lei ◽  
Jiong Zhang ◽  
Xiaoqing Tian ◽  
Jiang Han ◽  
Hao Wang
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Tool Path ◽  
Path Optimization ◽  
Machining Process ◽  
Axis Orientation ◽  
Surface Machining ◽  
Tool Path Optimization ◽  
Tool Axis ◽  
Planning Algorithms

Abstract This paper develops a tool path optimization method for robot surface machining by sampling-based motion planning algorithms. In the surface machining process, the tool-tip position needs to strictly follow the tool path curve and the posture of the tool axis should be limited in a certain range. But the industrial robot has at least six degrees of freedom (Dof) and has redundant Dofs for surface machining. Therefore, the tool motion of surface machining can be optimized using the redundant Dofs considering the tool path constraints and limits of the tool axis orientation. Due to the complexity of the problem, the sampling-based motion planning method has been chosen to find the solution, which randomly explores the configuration space of the robot and generates a discrete path of valid robot state. During the solving process, the joint space of the robot is chosen as the configuration space of the problem and the constraints for the tool-tip following requirements are in the operation space. Combined with general collision checking, the limited region of the tool axis vector is used to verify the state's validity of the configuration space. In the optimization process, the sum of path length of each joint of the robot is set as the optimization objective. The algorithm is developed based on the open motion planning library (OMPL) which contains the state-of-the-art sampling-based motion planners. Finally, two examples are used to demonstrate the effectiveness and optimality of the method.

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