Loading Dock Robot Vision Modeling for Ramp and Curtain Navigation
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Solution Overview
Problem
Mobile robots operating in loading dock environments face challenges in safely navigating and interacting with features like ramps and dock curtains due to limited situational awareness when outside a container, leading to potential collisions and reduced operational efficiency.
Innovation Solution
Equipping mobile robots with a camera system and a trained machine learning model to capture and process images of the loading dock environment, generating 3D representations of features, and controlling robot operations based on this information to safely drive and grasp objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If onboard distance sensors are used to perceive the container interior, then the robot can obtain information about the truck interior when located inside the container, but the sensors cannot sufficiently capture information about the loading dock environment when the robot is located outside the container
Solution Approach 1:
The camera system is designed to serve multiple functions: it captures images for both navigation when the robot is outside the container and for feature identification when inside the container. This multi-functional approach eliminates the need for separate specialized sensors for different operational phases, reducing overall system complexity while maintaining comprehensive environmental perception capabilities.
Solution Approach 2:
The system performs preliminary capture of loading dock environment information using the camera system before the robot enters the container. This advance perception allows the robot to plan its entry trajectory and understand the external environment, compensating for the inability of onboard distance sensors to capture external features when the robot is outside the container.
2Reliability
If the robot uses onboard sensors to navigate inside the container, then it can perceive the container interior, but the field of view of the onboard sensors may be obstructed when the robot wants to drive toward the entrance of the container
Solution Approach 1:
The camera system acts as an intermediary that captures visual information about the loading dock environment and container entrance from the robot's current position inside the container. This visual information is then processed to generate navigation commands, allowing the robot to navigate toward the entrance even when onboard distance sensors have obstructed field of view, thereby maintaining reliable navigation capability.
3Productivity
If the robot enters the container to unload objects, then it can access the cargo, but it cannot effectively grasp boxes while the base remains outside the container
Solution Approach 1:
The robot performs preliminary identification of container features such as the entrance location, internal obstacles, and cargo position using the camera system and machine learning model before entering the container. This advance understanding allows the robot to plan its entry trajectory and initial grasping actions, enabling it to effectively grasp boxes even when the base remains partially outside the container, thereby improving both productivity and ease of operation.
Data Source
AI summary
Methods and apparatus for operating a mobile robot in a loading dock environment are provided. The method comprises capturing, by a camera system of the mobile robot, at least one image of the loading dock environment, and processing, by at least one hardware processor of the mobile robot, the at least one image using a machine learning model trained to identify one or more features of the loading dock environment.