A combinatorial approach to planar non-colliding robot arm motion planning

2002 ◽  
Author(s):  
I. Streinu
Keyword(s):  
Motion Planning ◽  
Robot Arm ◽  
Combinatorial Approach ◽  
Arm Motion

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.

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.

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.

ROBOTIC HANDWRITING

2005 ◽  
Vol 02 (01) ◽  
pp. 105-124 ◽  
Author(s):  
VELJKO POTKONJAK
Keyword(s):  
Mathematical Models ◽  
Muscle Fatigue ◽  
Simulation Study ◽  
Robot Control ◽  
Robot Arm ◽  
Redundancy Resolution ◽  
Kinematic Redundancy ◽  
New Concepts ◽  
Human Arm ◽  
Arm Motion

Handwriting has always been considered an important human task, and accordingly it has attracted the attention of researchers working in biomechanics, physiology, and related fields. There exist a number of studies on this area. This paper considers the human–machine analogy and relates robots with handwriting. The work is two-fold: it improves the knowledge in biomechanics of handwriting, and introduces some new concepts in robot control. The idea is to find the biomechanical principles humans apply when resolving kinematic redundancy, express the principles by means of appropriate mathematical models, and then implement them in robots. This is a step forward in the generation of human-like motion of robots. Two approaches to redundancy resolution are described: (i) "Distributed Positioning" (DP) which is based on a model to represent arm motion in the absence of fatigue, and (ii) the "Robot Fatigue" approach, where robot movements similar to the movements of a human arm under muscle fatigue are generated. Both approaches are applied to a redundant anthropomorphic robot arm performing handwriting. The simulation study includes the issues of legibility and inclination of handwriting. The results demonstrate the suitability and effectiveness of both approaches.

Robot Trajectory Planning Using Rational Frenet-Serret Curves

Volume 2 ◽  
2004 ◽  
Author(s):  
Reza Ravani ◽  
Ali Meghdari
Keyword(s):  
Trajectory Planning ◽  
Computer Animation ◽  
Rational Curves ◽  
Robot Arm ◽  
Robot Trajectory ◽  
Arm Motion ◽  
Sweep Surface ◽  
And Robotics ◽  
Rotation Minimizing Frame ◽  
Robot Trajectory Planning

The aim of this paper is to demonstrate that the techniques of Computer Aided Geometric Design such as spatial rational curves and surfaces could be applied to Kinematics, Computer Animation and Robotics. For this purpose we represent a method which utilizes a special class of rational curves called Rational Frenet-Serret (RF) [8] curves for robot trajectory planning. RF curves distinguished by the property that the motion of their Frenet-Serret frame is rational. We describe an algorithm for interpolation of positions by a rational Frenet-Serret motion. Further more we provide an analysis on spatial frames (Frenet-Serret frame and Rotation Minimizing frame) for smooth robot arm motion and investigate their applications in sweep surface modeling.

Gradient Optimization of Inverse Dynamics for Robotic Manipulator Motion Planning Using Combined Optimal Control

2017 ◽  
Author(s):  
Shangdong Gong ◽  
Redwan Alqasemi ◽  
Rajiv Dubey
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Inverse Dynamics ◽  
Null Space ◽  
Optimal Solution ◽  
Human Motion ◽  
Optimization Techniques ◽  
Redundant Manipulators ◽  
Robot Arm ◽  
Gradient Optimization

Motion planning of redundant manipulators is an active and widely studied area of research. The inverse kinematics problem can be solved using various optimization methods within the null space to avoid joint limits, obstacle constraints, as well as minimize the velocity or maximize the manipulability measure. However, the relation between the torques of the joints and their respective positions can complicate inverse dynamics of redundant systems. It also makes it challenging to optimize cost functions, such as total torque or kinematic energy. In addition, the functional gradient optimization techniques do not achieve an optimal solution for the goal configuration. We present a study on motion planning using optimal control as a pre-process to find optimal pose at the goal position based on the external forces and gravity compensation, and generate a trajectory with optimized torques using the gradient information of the torque function. As a result, we reach an optimal trajectory that can minimize the torque and takes dynamics into consideration. We demonstrate the motion planning for a planar 3-DOF redundant robotic arm and show the results of the optimized trajectory motion. In the simulation, the torque generated by an external force on the end-effector as well as by the motion of every link is made into an integral over the squared torque norm. This technique is expected to take the torque of every joint into consideration and generate better motion that maintains the torques or kinematic energy of the arm in the safe zone. In future work, the trajectories of the redundant manipulators will be optimized to generate more natural motion as in humanoid arm motion. Similar to the human motion strategy, the robot arm is expected to be able to lift weights held by hands, the configuration of the arm is changed along from the initial configuration to a goal configuration. Furthermore, along with weighted least norm (WLN) solutions, the optimization framework will be more adaptive to the dynamic environment. In this paper, we present the development of our methodology, a simulated test and discussion of the results.

Sign in / Sign up

Export Citation Format

Share Document