Your English writing platform
Discover LudwigSuggestions(4)
Exact(2)
The flowchart of static and moving obstacle detection is shown in Fig. 3. Concerning moving obstacle detection, we employ the human detection module provided by Kinect SDK.
Open image in new window Fig. 3 Static and moving obstacle detection flowchart.
Similar(58)
The system, which incorporates time-dependent data, is composed of two main modules: (i) an effective ground surface estimation using a piecewise plane fitting algorithm and RANSAC-method, and (ii) a voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information.
Fig. 23 Moving obstacle representation case 2. Fig. 24 Avoidance of the moving obstacle towards the robot.
The experiments were divided into three cases: static obstacle, moving obstacle towards the robot, and moving obstacle intersecting the robot's path.
Fig. 25 Moving obstacle representation case 3 Fig. 26 Avoidance of the moving obstacle intersecting the robot Table 5 Avoidance of the moving obstacle intersecting the robot results Duration (s) Gain APT (ms) Normal 2.963 0.12 Modified 2.205 25.6%% 0.21.
A moving obstacle can obstruct the robot for a longer period of time if the path to avoid the obstacle ends, moving the robot parallel to the obstacle movement.
In the second case, the modifications made much more difference with a moving obstacle.
Open image in new window Fig. 23 Electrotactile representation of stationary and moving obstacle warning.
Figure 23 depicts the representation for stationary and moving obstacle warning for our system.
This zone is created around the projected direction of the moving obstacle.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com