The next-best-view for workpiece localization in robot workspace

2021 ◽  
Author(s):  
Jie Hu ◽  
Prabhakar R. Pagilla ◽  
Swaroop Darbha
Keyword(s):  
Workpiece Localization ◽  
Next Best View ◽  
Robot Workspace

scholarly journals Surface-driven Next-Best-View planning for exploration of large-scale 3D environments

2020 ◽  
Vol 53 (2) ◽  
pp. 15501-15507
Author(s):  
Guillaume Hardouin ◽  
Fabio Morbidi ◽  
Julien Moras ◽  
Julien Marzat ◽  
El Mustapha Mouaddib
Keyword(s):  
Large Scale ◽  
View Planning ◽  
3D Environments ◽  
Next Best View

Robot Workspace of a Tool Plane: Part 2—Computer Generation and Selected Design Conditions for Dexterity

1987 ◽  
Vol 109 (1) ◽  
pp. 61-71 ◽  
Author(s):  
J. K. Davidson ◽  
P. Pingali
Keyword(s):  
Ruled Surface ◽  
End Effector ◽  
Plane Part ◽  
Computer Generation ◽  
Robot Workspace

In this paper the algorithm is completed for generation of envelope-surfaces for plane-workspaces of generally proportioned manipulators. Then the ruled surface Ψ is used for adapting the algorithm to 3-R manipulators for which the outermost two axes intersect (a2 = 0). The discriminant D is further developed, and it is used to classify 3-R manipulators, having a2 = 0, into seven Types. Manipulators, which are of Type 7, (i) can provide any orientation to a tool plane σ or (ii), with a fourth appropriately placed R joint and tool plane Σ, can also provide any attitude to the end effector. Design conditions are developed and presented which ensure that a manipulator will possess these properties of dexterity. The conditions are based on coupled motions at all three, or four, axes.

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs

2021 ◽  
pp. 1-12
Author(s):  
Á. Martínez Novo ◽  
Liang Lu ◽  
Pascual Campoy
Keyword(s):  
State Of The Art ◽  
Micro Aerial Vehicles ◽  
Autonomous Exploration ◽  
Signed Distance ◽  
Aerial Vehicles ◽  
3D Environment ◽  
Frontier Points ◽  
Computational Resources ◽  
Next Best View ◽  
Signed Distance Field

This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.

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