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End-end-training process of PPCNet: Top row: The imitation learning and data aggregation processes for training the planner network. Bottom row: Population-based probability estimation and collision checker network training processes.

End-end-training process of PPCNet: Top row: The imitation learning and data aggregation processes for training the planner network. Bottom row: Population-based probability estimation and collision checker network training processes.

Source publication
Figure 1: Path planning for pick and place operation.
Figure 2: Path Planning and collision checking network (PPCNet).
Figure 3: Post processing procedure: a) The generated path by the...
Figure 4: End-end-training process of PPCNet: Top row: The imitation...
+1 Figure 5: Experimental environments: a) UR5 scene, b) UR5 scene with a...
End-to-end deep learning-based framework for path planning and collision checking: bin picking application
Preprint
Full-text available
  • Mar 2023
Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are i...
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Contexts in source publication

Context 1
... generate a high-quality dataset, post-processing is applied to the paths generated by the expert planner. As shown in Figure 4, the expert planner generates a collision-free path for a random query. Followed by that, the Binary State Contraction (BSC) is utilized to remove redundant and unnecessary waypoints, resulting in a shorter overall path. ...
Context 2
... that case, p true in Eq. 5 becomes either 0 (collision-free segment) or 1 (in-collision segment). In Figure 4 this approach has been named as L binary . The binary cross-entropy loss function measures the ...

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Figure 1. Path planning for pick-and-place operation.
Figure 2. Path planning and collision checking network (PPCNet).
End-to-end deep learning-based framework for path planning and collision checking: bin-picking application
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