GenMOS 3D Multi-Object Search Using Octree Ray-Tracing
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Solution Overview
Problem
Current robotic systems lack a general-purpose object search capability, particularly in 3D environments, due to computational complexity and limited field of view, occlusion, and unreliable object detectors, which hinders their ability to search for multiple objects simultaneously.
Innovation Solution
The GenMOS system employs a gRPC framework with a POMDP model and octree-based occupancy representation to enable robots to search for multiple objects in 3D regions by constructing an occupancy grid, using ray-tracing for visibility determination, and performing sequential decision-making for viewpoint planning, allowing robots to handle occlusions and uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general-purpose object search system is implemented in robots, then search capability and versatility are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the search problem into multiple manageable components: (1) occupancy grid construction for spatial representation, (2) ray-tracing for visibility determination, (3) POMDP framework for decision-making under uncertainty, and (4) sequential planning for action generation. Each component handles a specific aspect of the search task, allowing modular development and reduced overall complexity.
Solution Approach 2:
The patent introduces an intermediary POMDP agent that mediates between the robot's perception system and action system. This agent processes sensor data, maintains belief states, and generates actions sequentially, acting as a computational buffer that simplifies the interaction between complex subsystems and enables general-purpose search behavior.
2Productivity
If 3D multi-object search is performed in real-time, then search speed and productivity are improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-constructing the occupancy grid from sensor data and pre-calculating visibility information through ray-tracing before the actual search begins. This preparation allows the sequential POMDP planning to proceed more efficiently, as the computational heavy lifting is done in advance rather than in real-time during search.
Solution Approach 2:
The system dynamically adjusts the search strategy through sequential POMDP planning, where actions are planned step-by-step based on current observations and belief states. This dynamic approach allows the system to adapt to changing conditions and focus computational resources on the most promising search regions, improving efficiency compared to static search methods.
3Reliability
If occlusion handling is improved through advanced algorithms, then search reliability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical occlusion handling mechanisms with a computational approach using ray-tracing. Instead of physically manipulating sensors or using multiple camera systems to bypass occlusions, the system uses virtual ray-casting through the occupancy grid to determine visibility, substituting physical complexity with computational elegance.
Solution Approach 2:
The system handles occlusion by changing the representation parameters from raw sensor data to an occupancy grid format, where occluded regions are explicitly marked. This parameter transformation allows the POMDP agent to reason about occlusion in a simplified manner, improving reliability without proportionally increasing algorithmic complexity.
4Quantity of substance
If multiple objects are searched simultaneously in 3D space, then search completeness is improved, but field of view limitations and computational load increase
Solution Approach 1:
The system transitions from 2D image-based search to 3D volumetric search using an occupancy grid that represents the entire 3D space. This dimensional change allows the system to search for multiple objects simultaneously throughout the volume, overcoming the field of view limitations of 2D cameras by representing and searching the complete 3D environment.
Solution Approach 2:
The 3D search space is segmented into discrete occupancy grid cells, allowing the system to independently process and search multiple regions simultaneously. This segmentation enables parallel processing of different search regions, effectively handling multiple objects without proportionally increasing computational load, as each cell can be processed independently.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
GenMOS enables robots to efficiently search for and localize multiple objects in complex 3D environments, improving search success rates and reducing search time, while being adaptable to various environments and robots, with interpretable visualization of the search process.
Implementation Method 1
using ray-tracing to determine visibility at three dimensional locations within the search region
Data Source
AI summary
A method includes, in an automated machine equipped with one or more camera-based object detectors, receiving human-provided information or information inferred from point cloud observations regarding target locations, maintaining information states regarding the target locations through a probability distribution structured as an octree, initializing the information states based on point cloud observations, updating the information states based on object detection observations or point cloud observations, determining a search region occupancy through constructing an octree-based occupancy grid based on point cloud observations, and using ray-tracing to determine visibility at three dimensional locations within the search region.