EFFICIENT GRASP PLANNING WITH REACHABILITY ANALYSIS

2011 ◽  
Vol 08 (04) ◽  
pp. 761-775 ◽  
Author(s):  
ZHIXING XUE ◽  
RUEDIGER DILLMANN
Keyword(s):  
Efficient Algorithm ◽  
Reachability Analysis ◽  
Robotic Arm ◽  
Robotic Hands ◽  
Grasp Planning ◽  
Grasping Forces ◽  
Force Closure ◽  
Geometrical Information ◽  
Kinematic Information

Grasping can be seen as two steps: placing the hand at a grasping pose and closing the fingers. In this paper, we introduce an efficient algorithm for grasping pose generation. By the use of preshaping and eigen-grasping actions, the dimension of the space of possible hand configurations is reduced. The object to be grasped is decomposed into boxes of a discrete set of different sizes. By performing finger reachability analysis on the boxes, the kinematic feasibility of a grasp can be determined. If a reachable grasp is force-closure and can be performed by the robotic arm, its grasping forces are optimized and can be executed. The novelty of our algorithm is that it takes into account both the object geometrical information and the kinematic information of the hand to determine the grasping pose, so that a reachable grasping pose can be found very quickly. Real experiments with two different robotic hands show the efficiency and feasibility of our method.

Robotic, Grippers, Grasping, and Grasp Planning

2013 ◽  
pp. 277-300
Author(s):  
Seyed Javad Mousavi ◽  
Ellips Masehian
Keyword(s):  
New Classification ◽  
Robotic Hands ◽  
Grasp Planning ◽  
Force Closure ◽  
Form Closure ◽  
Basic Concepts ◽  
2D And 3D ◽  
Grasp Quality ◽  
Planning Approaches

Utilizing robotic hands for manipulating objects and assembly requires one to deal with problems like immobility, grasp planning, and regrasp planning. This chapter integrates some essential subjects on robotic grasping: the first section presents a concise taxonomy of robotic grippers and hands. Then the basic concepts of grasping are provided, including immobility, form-closure, and force-closure, 2D and 3D grasping, and Coulomb friction. Next, the principles of grasp planning, measures of grasping quality, pre-grasp, stable grasps, and regrasp planning are presented. The chapter presents comparisons for robotic grippers, a new classification of measures of grasp quality, and a new categorization of regrasp planning approaches.

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

Hand closure model for planning top grasps with soft robotic hands

2020 ◽  
Vol 39 (14) ◽  
pp. 1706-1723
Author(s):  
Maria Pozzi ◽  
Sara Marullo ◽  
Gionata Salvietti ◽  
Joao Bimbo ◽  
Monica Malvezzi ◽  
...  
Keyword(s):  
Robotic Systems ◽  
Closure Model ◽  
Robotic Hands ◽  
Grasp Planning ◽  
Model Based ◽  
Major Trend ◽  
Different Shapes ◽  
Planning Strategies ◽  
Experimental Trials ◽  
Soft Manipulation

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

Automated Design of Robotic Hands for In-Hand Manipulation Tasks

2020 ◽  
Vol 17 (01) ◽  
pp. 1950029
Author(s):  
Christopher Hazard ◽  
Nancy Pollard ◽  
Stelian Coros
Keyword(s):  
Kinematic Model ◽  
Automated Design ◽  
Robotic Hand ◽  
Motion Synthesis ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Grasp Planning ◽  
User Input ◽  
High Level

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

Workspace and Reachability Analysis of a Robotic Arm for Sample Cache Retrieval from a Mars Rover

2015 ◽  
Author(s):  
Kyle Edelberg ◽  
Dennis Wai ◽  
Jason Reid ◽  
Eric Kulczycki ◽  
Paul Backes
Keyword(s):  
Reachability Analysis ◽  
Robotic Arm ◽  
Mars Rover

Intelligent grasp planning strategy for robotic hands

1991 ◽  
Author(s):  
Ian D. Walker ◽  
John B. Cheatham, Jr. ◽  
Yu-Che Chen
Keyword(s):  
Robotic Hands ◽  
Grasp Planning ◽  
Planning Strategy

Path finding and grasp planning for robotic assembly

Robotica ◽  
1994 ◽  
Vol 12 (4) ◽  
pp. 353-360
Author(s):  
Byung R. Lee ◽  
Paul I. Ro
Keyword(s):  
Efficient Algorithm ◽  
Robotic Assembly ◽  
Assembly Process ◽  
Robot Arm ◽  
Grasp Planning ◽  
Male And Female ◽  
Planning Strategy ◽  
Female Part ◽  
Male Part ◽  
Proper Performance

SUMMARYThis paper focuses on developing a planning strategy for robotic assembly. At first, channel and junction are defined by using half-spaces, and free-space inside the female part is approximately decomposed by channels and junctions. Then, a simple and efficient algorithm to find the assembly path of the male part is developed, in which any path between the shortest and safest paths can be easily found by just changing the clearance gap between the male and female parts. Next, the robot arm is considered in the path planning, in which a feasible grasp angle region is obtained to avoid a collision between the robot arm and the female part during the assembly process. An optimum grasping angle can be found in the feasible grasping angle region by applying a proper performance index. Finally, a simple robotic assembly using the algorithm is numerically demonstrated.

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