scholarly journals Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 395 ◽  
Author(s):  
Fusheng Zha ◽  
Yizhou Liu ◽  
Wei Guo ◽  
Pengfei Wang ◽  
Mantian Li ◽  
...  
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Configuration Space ◽  
Fundamental Problem ◽  
Approximate Model ◽  
High Dimensional ◽  
Constrained Motion ◽  
Task Constraint ◽  
Task Constraints ◽  
Planning Methods

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.

Tangent bundle RRT: A randomized algorithm for constrained motion planning

Robotica ◽  
2014 ◽  
Vol 34 (1) ◽  
pp. 202-225 ◽  
Author(s):  
Beobkyoon Kim ◽  
Terry Taewoong Um ◽  
Chansu Suh ◽  
F. C. Park
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Tangent Bundle ◽  
Numerical Experiments ◽  
Randomized Algorithm ◽  
Random Trees ◽  
Constrained Motion ◽  
Wide Range ◽  
Random Node ◽  
Planning Problems

SUMMARYThe Tangent Bundle Rapidly Exploring Random Tree (TB-RRT) is an algorithm for planning robot motions on curved configuration space manifolds, in which the key idea is to construct random trees not on the manifold itself, but on tangent bundle approximations to the manifold. Curvature-based methods are developed for constructing tangent bundle approximations, and procedures for random node generation and bidirectional tree extension are developed that significantly reduce the number of projections to the manifold. Extensive numerical experiments for a wide range of planning problems demonstrate the computational advantages of the TB-RRT algorithm over existing constrained path planning algorithms.

Parallel computation of configuration space

Robotica ◽  
1996 ◽  
Vol 14 (2) ◽  
pp. 205-212 ◽  
Author(s):  
J. Solano González ◽  
D.I. Jonest
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Parallel Computer ◽  
Computer Language ◽  
Parallel Tasks ◽  
Planning Methods ◽  
Network Topologies ◽  
Speed Up ◽  
Computationally Intensive ◽  
Proper Account

SUMMARYMany motion planning methods use Configuration Space to represent a robot manipulator's range of motion and the obstacles which exist in its environment. The Cartesian to Configuration Space mapping is computationally intensive and this paper describes how the execution time can be decreased by using parallel processing. The natural tree structure of the algorithm is exploited to partition the computation into parallel tasks. An implementation programmed in the occam2 parallel computer language running on a network of INMOS transputers is described. The benefits of dynamically scheduling the tasks onto the processors are explained and verified by means of measured execution times on various processor network topologies. It is concluded that excellent speed-up and efficiency can be achieved provided that proper account is taken of the variable task lengths in the computation.

scholarly journals Asymptotically Optimal Sampling-Based Motion Planning Methods

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jonathan D. Gammell ◽  
Marlin P. Strub
Keyword(s):  
Motion Planning ◽  
Fundamental Problem ◽  
Autonomous Systems ◽  
Optimal Solution ◽  
Computational Effort ◽  
Annual Review ◽  
Publication Date ◽  
Ongoing Research ◽  
Asymptotically Optimal ◽  
Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Task constrained motion planning for 7-degree of freedom manipulators with parameterized submanifolds

2018 ◽  
Vol 45 (3) ◽  
pp. 363-370 ◽  
Author(s):  
Qiang Qiu ◽  
Qixin Cao
Keyword(s):  
Motion Planning ◽  
Explicit Expression ◽  
Collision Detection ◽  
Tool Path ◽  
Degree Of Freedom ◽  
Geometric Constraints ◽  
Constrained Motion ◽  
Content Type ◽  
Planning Methods ◽  
Two Parameters

PurposeThis paper aims to use the redundancy of a 7-DOF (degree of freedom) serial manipulator to solve motion planning problems along a given 6D Cartesian tool path, in the presence of geometric constraints, namely, obstacles and joint limits.Design/methodology/approachThis paper describes an explicit expression of the task submanifolds for a 7-DOF redundant robot, and the submanifolds can be parameterized by two parameters with this explicit expression. Therefore, the global search method can find the feasible path on this parameterized graph.FindingsThe proposed planning algorithm is resolution complete and resolution optimal for 7-DOF manipulators, and the planned path can satisfy task constraint as well as avoiding singularity and collision. The experiments on Motoman SDA robot are reported to show the effectiveness.Research limitations/implicationsThis algorithm is still time-consuming, and it can be improved by applying parallel collision detection method or lazy collision detection, adopting new constraints and implementing more effective graph search algorithms.Originality/valueCompared with other task constrained planning methods, the proposed algorithm archives better performance. This method finds the explicit expression of the two-dimensional task sub-manifolds, so it’s resolution complete and resolution optimal.

scholarly journals Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning

2018 ◽  
Author(s):  
Fahad Islam ◽  
Oren Salzman ◽  
Maxim Likhachev
Keyword(s):  
Motion Planning ◽  
Domain Knowledge ◽  
High Dimensional ◽  
Constrained Motion ◽  
Significant Progress ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Speed Up ◽  
User Guidance ◽  
Intermediate Configuration

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration q^, which is used to bias the planner to go through q^. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafted domain-dependent heuristics.

Sampling-Based Methods for Motion Planning with Constraints

2018 ◽  
Vol 1 (1) ◽  
pp. 159-185 ◽  
Author(s):  
Zachary Kingston ◽  
Mark Moll ◽  
Lydia E. Kavraki
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Planning Process ◽  
Disaster Relief ◽  
Humanoid Robots ◽  
High Dimensional ◽  
Mobile Manipulators ◽  
Continuous Motion ◽  
Task Constraints ◽  
Planning Algorithms

Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

scholarly journals Chance Constrained Motion Planning for High-Dimensional Robots

2019 ◽  
Author(s):  
Siyu Dai ◽  
Shawn Schaffert ◽  
Ashkan Jasour ◽  
Andreas Hofmann ◽  
Brian Williams
Keyword(s):  
Motion Planning ◽  
High Dimensional ◽  
Constrained Motion

scholarly journals Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Fusheng Zha ◽  
Yizhou Liu ◽  
Xin Wang ◽  
Fei Chen ◽  
Jingxuan Li ◽  
...  
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Probabilistic Model ◽  
Environmental Changes ◽  
Dimensional Space ◽  
Model Fitting ◽  
High Dimensional ◽  
Planning Problem ◽  
Robot Configuration ◽  
Model Training

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency.

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.

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