On motion planning with six degrees of freedom: Solving the intersection problems in configuration space

2005 ◽  
Author(s):  
B. Donald
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Six Degrees Of Freedom

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.

Motion planning in crowded planar environments

Robotica ◽  
1999 ◽  
Vol 17 (4) ◽  
pp. 365-371 ◽  
Author(s):  
Yoav Lasovsky ◽  
Leo Joskowicz
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Local Geometry ◽  
Objective Functions ◽  
Linear Optimization Problems ◽  
Geometrically Complex ◽  
Crowded Environments

We present a new algorithm for fine motion planning in geometrically complex situations. Geometrically complex situations have complex robot and environment geometry, crowded environments, narrow passages and tight fits. They require complex robot motions with coupled degrees of freedom. The algorithm constructs a path by incrementally building a graph of linearized convex configuration space cells and solving a series of linear optimization problems with varying objective functions. Its advantages are that it better exploits the local geometry of narrow passages in configuration space, and that its complexity does not significantly increase as the clearance of narrow passages decreases. We demonstrate the algorithm on examples which other planners could not solve.

Trajectory Prediction with a Conditional Variational Autoencoder

2019 ◽  
Vol 31 (3) ◽  
pp. 493-499
Author(s):  
Thibault Barbié ◽  
◽  
Takaki Nishio ◽  
Takeshi Nishida
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Industrial Robot ◽  
Computation Time ◽  
Prediction Algorithm ◽  
Robot Arm ◽  
Trajectory Prediction ◽  
Six Degrees Of Freedom ◽  
Variational Autoencoder ◽  
Planning Approaches

Conventional motion planners do not rely on previous experience when presented with a new problem. Trajectory prediction algorithms solve this problem using a pre-existing dataset at runtime. We propose instead using a conditional variational autoencoder (CVAE) to learn the distribution of the motion dataset and hence to generate trajectories for use as priors within the traditional motion planning approaches. We demonstrate, through simulations and by using an industrial robot arm with six degrees of freedom, that our trajectory prediction algorithm generates more collision-free trajectories compared to the linear initialization, and reduces the computation time of optimization-based planners.

Repetitive Motion Planning and Control on Redundant Robot Manipulators With Push-Rod-Type Joints

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Z. Zhang ◽  
Y. Zhang
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Quadratic Program ◽  
Error Feedback ◽  
Repetitive Motion ◽  
Six Degrees Of Freedom ◽  
Planning And Control ◽  
Joint Velocity ◽  
And Control ◽  
Joint Limit

To demonstrate the hardware realizability and efficacy of the quadratic program (QP) based methods for solving the nonrepetitive problem, this paper proposes a novel repetitive motion planning and control (RMPC) scheme and realizes this scheme on a physical planar six degrees-of-freedom (DOF) push-rod-joint (PRJ) manipulator. To control the PRJ manipulator, this scheme considers variable joint-velocity limits and joint-limit margins. In addition, to decrease the errors, this scheme considers the position-error feedback. Then, the scheme is reformulated as a QP problem. Due to control of the digital computer, a discrete-time QP solver is presented to solve the QP problem. For comparison, both of the nonrepetitive and repetitive motions are performed on the manipulator to track square and B-shaped paths. Experimental results validate the physical realizability and effectiveness of the RMPC scheme.

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1327-1335 ◽  
Author(s):  
Nir Shvalb ◽  
Boaz Ben Moshe ◽  
Oded Medina
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Morse Function ◽  
Climbing Robot ◽  
Probabilistic Algorithm ◽  
Road Map ◽  
Time Motion ◽  
Planning Algorithm

SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

A Surface Integral Approach to the Motion Planning of Nonholonomic Systems

1994 ◽  
Vol 116 (3) ◽  
pp. 315-325 ◽  
Author(s):  
Ranjan Mukherjee ◽  
David P. Anderson
Keyword(s):  
Motion Planning ◽  
Surface Area ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Nonholonomic Systems ◽  
Integral Approach ◽  
Surface Integral ◽  
Independent Variables ◽  
Dependent Variables ◽  
Disk Rolling

Nonholonomic mechanical systems are governed by constraints of motion that are nonintegrable differential expressions. Unlike holonomic constraints, these constraints do not reduce the number of dimensions of the configuration space of a system. Therefore a nonholonomic system can access a configuration space of dimension higher than the number of the degrees of freedom of the system. In this paper, we develop an algorithm for planning admissible trajectories for nonholonomic systems that will take the system from one point in its configuration space to another. In our algorithm the independent variables are first converged to their desired values. Subsequently, closed trajectories of the independent variables are used to converge the dependent variables. We use Green’s theorem in our algorithm to convert the problem of finding a closed path into that of finding a surface area in the space of the independent variables such that the dependent variables converge to their desired values as the independent variables traverse along the boundary of this surface area. Using this approach, we specifically address issues related to the reachability of the system, motion planning amidst additional constraints, and repeatable motion of nonholonomic systems. The salient features of our algorithm are quite apparent in the two examples we discuss: a planar space robot and a disk rolling without slipping on a flat surface.

Sign in / Sign up

Export Citation Format

Share Document