MASSIVELY PARALLEL IDA* SEARCH

1993 ◽  
Vol 02 (02) ◽  
pp. 163-180 ◽  
Author(s):  
DIANE J. COOK ◽  
GARY LYONS
Keyword(s):  
Artificial Intelligence ◽  
Motion Planning ◽  
Heuristic Search ◽  
Massively Parallel ◽  
Search Process ◽  
Planning Problem ◽  
Robot Arm ◽  
Speed Up ◽  
Arm Motion ◽  
Computational Bottleneck

Heuristic search is a fundamental component of Artificial Intelligence applications. Because search routines are frequently also a computational bottleneck, numerous methods have been explored to increase the efficiency of search. Recently, researchers have begun investigating methods of using parallel MIMD and SIMD hardware to speed up the search process. In this paper, we present a massively-parallel SIMD approach to search named MIDA* search. The components of MIDA* include a very fast distribution algorithm which biases the search to one side of the tree, and an incrementally-deepening depthfirst search of all the processors in parallel. We show the results of applying MIDA* to instances of the Fifteen Puzzle problem and to the robot arm motion planning problem. Results reveal an efficiency of 74% and a speedup of 8553 and 492 over serial and 16-processor MIMD algorithms, respectively, when finding a solution to the Fifteen Puzzle problem that is close to optimal.

scholarly journals Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization

2020 ◽  
Author(s):  
Wei-Ye Zhao ◽  
Suqin He ◽  
Chengtao Wen ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Motion Planning ◽  
Real Time ◽  
Trajectory Generation ◽  
Robot Kinematics ◽  
Planning Problem ◽  
Robot Arm ◽  
Task Constraints ◽  
Robot Arms ◽  
Highly Nonlinear

Abstract Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.

Humanoid Arm Motion Planning Using Stereo Vision and RRT Search

2003 ◽  
Vol 15 (2) ◽  
pp. 200-207 ◽  
Author(s):  
Satoshi Kagami ◽  
◽  
James J. Kuffner ◽  
Koichi Nishiwaki ◽  
Kei Okada ◽  
...  
Keyword(s):  
Motion Planning ◽  
Stereo Vision ◽  
Humanoid Robots ◽  
Random Trees ◽  
Planning System ◽  
Robot Arm ◽  
Consistency Checking ◽  
Disparity Map ◽  
Arm Motion ◽  
High Level

This paper describes an experimental stereo vision based motion planning system for humanoid robots. The goal is to automatically generate arm trajectories that avoid obstacles in unknown environments from high-level task commands. Our system consists of three components: 1) environment sensing using stereo vision with disparity map generation and online consistency checking, 2) probabilistic mesh modeling in order to accumulate continuous vision input, and 3) motion planning for the robot arm using RRTs (Rapidly exploring Random Trees). We demonstrate results from experiments using an implementation designed for the humanoid robot H7.

Collision-free motion planning for two articulated robot arms using minimum distance functions

Robotica ◽  
1990 ◽  
Vol 8 (2) ◽  
pp. 137-144 ◽  
Author(s):  
C. Chang ◽  
M. J. Chung ◽  
Z. Bien
Keyword(s):  
Motion Planning ◽  
Minimum Distance ◽  
Three Dimensional ◽  
Distance Functions ◽  
Free Motion ◽  
Planning Problem ◽  
Robot Arm ◽  
Robot Arms ◽  
Nonlinear Minimization ◽  
Articulated Robot

SummaryThis paper presents a collision-free motion planning method of two articulated robot arms in a three dimensional common work space. Each link of a robot arm is modeled by a cylinder ended by two hemispheres, and the remaining wrist and hand is modeled by a sphere. To describe the danger of collision between two modeled objects, minimum distance functions, which are defined by the Euclidean norm, are used. These minimum distance functions are used to describe the constraints that guarantee no collision between two robot arms. The collision-free motion planning problem is formulated as a pointwise constrained nonlinear minimization problem, and solved by a conjugate gradient method with barrier functions. To improve the minimization process, a simple grid technique is incorporated. Finally, a simulation study is presented to show the significance of the proposed method.

Deep learning can accelerate grasp-optimized motion planning

2020 ◽  
Vol 5 (48) ◽  
pp. eabd7710
Author(s):  
Jeffrey Ichnowski ◽  
Yahav Avigal ◽  
Vishal Satish ◽  
Ken Goldberg
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Computation Time ◽  
Robot Arm ◽  
Optimal Motion ◽  
Asymptotically Optimal ◽  
Arm Motions ◽  
Motion Time ◽  
Starting Point ◽  
Arm Motion

Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning–based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.

scholarly journals Adaptive Parallel Iterative Deepening Search

1998 ◽  
Vol 9 ◽  
pp. 139-165 ◽  
Author(s):  
D. J. Cook ◽  
R. C. Varnell
Keyword(s):  
Heuristic Search ◽  
Search Strategy ◽  
Search Time ◽  
Search Space ◽  
Computational Effort ◽  
Machine Learning Techniques ◽  
Parallel Search ◽  
Robot Arm ◽  
Problem Space ◽  
Arm Motion

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.

scholarly journals Single- and dual-arm motion planning with heuristic search

2013 ◽  
Vol 33 (2) ◽  
pp. 305-320 ◽  
Author(s):  
Benjamin Cohen ◽  
Sachin Chitta ◽  
Maxim Likhachev
Keyword(s):  
Motion Planning ◽  
Heuristic Search ◽  
Arm Motion ◽  
Dual Arm

scholarly journals Normal form approach in the motion planning of space robots: a case study

2021 ◽  
Author(s):  
Krzysztof Tchoń ◽  
Katarzyna Zadarnowska
Keyword(s):  
Normal Form ◽  
Motion Planning ◽  
Normal Forms ◽  
Inverse Dynamics ◽  
Planning Problem ◽  
Inverse Dynamics Problem ◽  
Velocity Level ◽  
Form Approach ◽  
Space Approach

AbstractWe examine applicability of normal forms of non-holonomic robotic systems to the problem of motion planning. A case study is analyzed of a planar, free-floating space robot consisting of a mobile base equipped with an on-board manipulator. It is assumed that during the robot’s motion its conserved angular momentum is zero. The motion planning problem is first solved at velocity level, and then torques at the joints are found as a solution of an inverse dynamics problem. A novelty of this paper lies in using the chained normal form of the robot’s dynamics and corresponding feedback transformations for motion planning at the velocity level. Two basic cases are studied, depending on the position of mounting point of the on-board manipulator. Comprehensive computational results are presented, and compared with the results provided by the Endogenous Configuration Space Approach. Advantages and limitations of applying normal forms for robot motion planning are discussed.

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