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.

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.

Study on dual peg-in-hole insertion using of constraints formed in the environment

2017 ◽  
Vol 44 (6) ◽  
pp. 730-740 ◽  
Author(s):  
Jianhua Su ◽  
Rui Li ◽  
Hong Qiao ◽  
Jing Xu ◽  
Qinglin Ai ◽  
...  
Keyword(s):  
Configuration Space ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Force Sensor ◽  
High Dimensional ◽  
Content Type ◽  
Attractive Region ◽  
Low Dimensional ◽  
The Cost ◽  
Yaw Angle

Purpose The purpose of this paper is to develop a dual peg-in-hole insertion strategy. Dual peg-in-hole insertion is the most common task in manufacturing. Most of the previous work develop the insertion strategy in a two- or three-dimensional space, in which they suppose the initial yaw angle is zero and only concern the roll and pitch angles. However, in some case, the yaw angle could not be ignored due to the pose uncertainty of the peg on the gripper. Therefore, there is a need to design the insertion strategy in a higher-dimensional configuration space. Design/methodology/approach In this paper, the authors handle the insertion problem by converting it into several sub-problems based on the attractive region formed by the constraints. The existence of the attractive region in the high-dimensional configuration space is first discussed. Then, the construction of the high-dimensional attractive region with its sub-attractive region in the low-dimensional space is proposed. Therefore, the robotic insertion strategy can be designed in the subspace to eliminate some uncertainties between the dual pegs and dual holes. Findings Dual peg-in-hole insertion is realized without using of force sensors. The proposed strategy is also used to demonstrate the precision dual peg-in-hole insertion, where the clearance between the dual-peg and dual-hole is about 0.02 mm. Practical implications The sensor-less insertion strategy will not increase the cost of the assembly system and also can be used in the dual peg-in-hole insertion. Originality/value The theoretical and experimental analyses for dual peg-in-hole insertion are proposed without using of force sensor.

scholarly journals Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2759 ◽  
Author(s):  
Jiankun Wang ◽  
Max Q.-H. Meng
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Dimensional Space ◽  
Optimal Path ◽  
High Dimensional ◽  
Force Model ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Social Force ◽  
The Social

This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning problem is formulated in this paper as a Traveling Salesman Problem (TSP). Different from the conventional solutions to the TSP, in which the Euclidean distance between two sites is used as the metric, a high-dimensional metric including the factor of pedestrians’ feelings is applied in this work. To obtain the new metric, a novel potential function is firstly proposed to model the relationship between the robot, luggage trolleys, obstacles, and pedestrians. The Social Force Model (SFM) is utilized so that the pedestrians can bring extra influence on the potential field, different from ordinary obstacles. Directed by the attractive and repulsive force generated from the potential field, a number of paths connecting the robot and the luggage trolley, or two luggage trolleys, can be obtained. The length of the generated path is considered as the new metric. The Self-Organizing Map (SOM) satisfies the job of finding a final path to connect all luggage trolleys and the robot located in the potential field, as it can find the intrinsic connection in the high dimensional space. Therefore, while incorporating the new metric, the SOM is used to find the optimal path in which the robot can collect the assigned luggage trolleys in sequence. As a demonstration, the proposed path planning method is implemented in simulation experiments, showing an increase of efficiency and efficacy.

Motion Planning with Complete Knowledge Using a Colored Som

1997 ◽  
Vol 08 (05n06) ◽  
pp. 613-628 ◽  
Author(s):  
Jules Vleugels ◽  
Joost N. Kok ◽  
Mark Overmars
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
Configuration Space ◽  
Second Phase ◽  
Planning Problem ◽  
Self Organizing Map ◽  
The Road ◽  
Road Map ◽  
Complete Knowledge ◽  
Colored Version

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space. In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively. In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot. The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.

scholarly journals CATEGORY AND TOPOLOGICAL COMPLEXITY OF THE CONFIGURATION SPACE

2019 ◽  
Vol 100 (3) ◽  
pp. 507-517
Author(s):  
CESAR A. IPANAQUE ZAPATA
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Rigid Bodies ◽  
Robot Motion ◽  
Planning Problem ◽  
Topological Complexity ◽  
Robot Motion Planning ◽  
Spatial Motion ◽  
Lusternik Schnirelmann ◽  
Homotopy Invariants

The Lusternik–Schnirelmann category cat and topological complexity TC are related homotopy invariants. The topological complexity TC has applications to the robot motion planning problem. We calculate the Lusternik–Schnirelmann category and topological complexity of the ordered configuration space of two distinct points in the product $G\times \mathbb{R}^{n}$ and apply the results to the planar and spatial motion of two rigid bodies in $\mathbb{R}^{2}$ and $\mathbb{R}^{3}$ respectively.

scholarly journals Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

2016 ◽  
Vol 36 (1) ◽  
pp. 44-67 ◽  
Author(s):  
Quang-Cuong Pham ◽  
Stéphane Caron ◽  
Puttichai Lertkultanon ◽  
Yoshihiko Nakamura
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Geometric Constraints ◽  
High Dimensional ◽  
Planning Problem ◽  
Admissible Velocity ◽  
Velocity Decomposition ◽  
Powerful Approach ◽  
Planning Problems ◽  
Fundamental Requirement

Path-velocity decomposition is an intuitive yet powerful approach to addressing the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler steps: first, a path is found in the configuration space that satisfies the geometric constraints (path planning), and second, a time-parameterization of that path satisfying the kinodynamic constraints is found. A fundamental requirement is that the path found in the first step must be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints during the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm – Admissible Velocity Propagation – which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path, while respecting the system’s kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail.

scholarly journals On dynamically consistent Jacobian inverse for non-holonomic robotic systems

2017 ◽  
Vol 27 (4) ◽  
pp. 555-573 ◽  
Author(s):  
Joanna Ratajczak ◽  
Krzysztof Tchoń
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Consistency Condition ◽  
Robotic Systems ◽  
Planning Problem ◽  
Commutativity Property ◽  
Kinematic Singularities ◽  
Planning Problems ◽  
Jacobian Inverse ◽  
Space Approach

AbstractThis paper presents the dynamically consistent Jacobian inverse for non-holonomic robotic system, and its application to solving the motion planning problem. The system’s kinematics are represented by a driftless control system, and defined in terms of its input-output map in accordance with the endogenous configuration space approach. The dynamically consistent Jacobian inverse (DCJI) has been introduced by means of a Riemannian metric in the endogenous configuration space, exploiting the reduced inertia matrix of the system’s dynamics. The consistency condition is formulated as the commutativity property of a diagram of maps. Singular configurations of DCJI are studied, and shown to coincide with the kinematic singularities. A parametric form of DCJI is derived, and used for solving example motion planning problems for the trident snake mobile robot. Some advantages in performance of DCJI in comparison to the Jacobian pseudoinverse are discovered.

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.

Motion Planning in Plant CAD Systems

1991 ◽  
Author(s):  
Koichi Kondo ◽  
Koichi Ohtomi
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Free Space ◽  
Nuclear Power ◽  
Outer Boundary ◽  
Global Strategy ◽  
Planning Problem ◽  
Cad System ◽  
Local Strategy ◽  
Stationary Obstacles

Abstract This paper discusses a general and efficient method for solving the motion planning problem defined as checking the existence of a collision-free path among known stationary obstacles, and also presents its application to a plant CAD system. The basic approach taken in this method is to restrict the free space referred to in path searching and to avoid executing unnecessary collision detections. The configuration space is equally quantized into cells by placing a regular grid, and two new search strategies which enumerate restricted cells are introduced for realizing this method. One is a local strategy which enumerates free space cells only along the boundary of the free space in the configuration space. Another is a global strategy which finds the outer boundary of the free space. This method has been actually implemented and has been applied to an example in a nuclear power plant.

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