Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

Wong, Ching‐Chang; Yeh, Li-Yu; Liu, Chih‐Cheng; Tsai, Chi-Yi; Aoyama, H.

doi:10.3390/s21072280

Cited by 20 publications

(13 citation statements)

References 29 publications

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“…The LCC process begins at node A connected to the power supply and ends when a connection is created to the node connected to the GND. A closeup view of the image segment shows the analysis to follow, where from node A (positioned at coordinates (5,4)), the next segment of the path with the greatest current is determined employing a local comparison with each of the neighbors (nodes: (4, 4), (4, 3), (5,3), (6,3)). Notably, the LCC algorithm depends on the prior knowledge of the electrical variables' values and all resistors in the resistive network.…”

Section: Resistive Grid-based Planning Methodsmentioning

confidence: 99%

“…This information provides both the positions of the robot and the surrounding objects. Commonly, the information about the environment is provided by sensors onboard the robotic agent or by 3D cameras placed at strategic points in the robot’s workspace [ 3 ]. Figure 2 shows the basic architecture of an intelligent system based on a robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

“…For reactive planning, the robot has a perception system that allows it to know the environment in which it performs its task, and its main application is for environments with dynamic obstacles. For map-based planning, an RGB-D camera or 3D laser sensor provides the map i.e., the workspace is known a priori and obstacles are considered static objects [ 3 ]. For the reactive planning approach, a smoothly RRT (S-RRT) algorithm was proposed [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…Using the same approach, ref. [ 3 ] proposed an algorithm to obtain a 3D workspace representation and compute a reorientation object path using a manipulator. For map-based planners, the following works can be found: First, for low-DOF and low obstacles density [ 22 ], employed a path planner based on a multi-resolution potential field to calculate collision-free paths for a four-DOF planar manipulator in 2D workspaces.…”

Section: Introductionmentioning

confidence: 99%

“…(2) These are considered complete resolution planners, i.e., if a solution exists, any of these algorithms can obtain one; otherwise, the method terminates and reports that no solution exists for the specified resolution [4]. (3) The representation of the obstacles is straightforward since occupation cells' representation is used. (4) The C space is easily stored in a bitmap.…”

Section: Introductionmentioning

confidence: 99%

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Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators

Huerta-Chua

Diaz-Arango

Vázquez-Leal

et al. 2021

Sensors

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The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.

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