Mobile Robot Multi-Object Search with OO-POMDP Belief Updates
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Solution Overview
Problem
Existing robotic systems face challenges in performing multi-object search tasks under uncertainty, as they struggle with large and complex environments, imprecise sensors, and dynamic conditions, making existing approaches computationally intractable for planning.
Innovation Solution
The implementation of an Object-Oriented Partially Observable Markov Decision Process (OO-POMDP) framework, which represents states and observations in terms of classes and objects with attributes and semantic labels, allowing for efficient reasoning and updates based on language commands and sensor observations, using the OO-POMCP algorithm for online planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing sequential decision making approaches are used for multi-object search, then robots can operate under uncertainty, but the computational complexity becomes intractable for large domains
Solution Approach 1:
The patent segments the complex multi-object search problem into independent object-level subproblems. Each object has its own belief representation and search strategy, allowing the robot to reason about multiple objects separately rather than as a monolithic state space. This segmentation makes the computational problem tractable while maintaining reliable decision-making under uncertainty.
Solution Approach 2:
The patent changes the parameter representation from full state-space to object-level beliefs. By representing uncertainty as probability distributions over object locations rather than complete state configurations, the system reduces computational complexity while preserving the ability to make reliable decisions about where to search next.
2Adaptability or versatility
If robots use imprecise sensors and lack full knowledge of environment, then they can operate in real-world conditions, but uncertainty increases making planning difficult
Solution Approach 1:
The patent implements feedback through continuous sensor observations that update object beliefs. As the robot moves and sensors detect objects or fail to detect them, the belief distributions are updated accordingly. This feedback loop allows the robot to adapt to real-world conditions while systematically reducing uncertainty about object locations.
Solution Approach 2:
The patent performs preliminary actions by proactively searching high-probability locations before uncertainty fully develops. The robot uses current belief distributions to identify and search locations most likely to contain objects, preventing information loss by acting while uncertainty is still manageable.
3Reliability
If robots search for multiple objects in large environments, then task completeness improves, but search time and computational resources increase
Solution Approach 1:
The patent segments the multi-object search into independent object hypotheses, allowing parallel reasoning about multiple targets. Instead of exhaustively searching the entire environment for each object sequentially, the system maintains and updates beliefs about multiple objects simultaneously, significantly reducing total search time while ensuring task completeness.
Solution Approach 2:
The patent applies partial action by focusing search efforts on high-probability regions rather than exhaustively searching all possible locations. The robot performs sufficient search actions to achieve task completeness based on belief thresholds, avoiding unnecessary exploration of low-probability areas and reducing overall search time.
Data Source
AI summary
A system and method of operating a mobile robot to perform tasks includes representing a task in an Object-Oriented Partially Observable Markov Decision Process model having at least one belief pertaining to a state and at least one observation space within an environment, wherein the state is represented in terms of classes and objects and each object has at least one attribute and a semantic label. The method further includes receiving a language command identifying a target object and a location corresponding to the target object, updating the belief associated with the target object based on the language command, driving the mobile robot to the observation space identified in the updated belief, searching the updated observation space for each instance of the target object, and providing notification upon completing the task. In an embodiment, the task is a multi-object search task.