Obstacle recognition method for autonomous robots
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Solution Overview
Problem
Autonomous robots face challenges in efficiently navigating and performing tasks within dynamic environments due to limitations in mapping, localization, object recognition, and path planning, which hinders their ability to adapt to changing conditions and interact effectively with their surroundings.
Innovation Solution
A robot equipped with a processor, image sensor, and object classification unit that captures images, compares them to an object dictionary, and adjusts its actions based on identified objects, allowing for dynamic navigation and task execution, including movement, scheduling, and spatial modifications within the workspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mapping and localization methods are used, then the robot can navigate basic environments, but it cannot adapt to dynamic conditions and changing surroundings
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the robot's internal model of the environment based on real-time sensor data. The system transitions from static pre-programmed navigation to dynamic re-planning, where the robot can adjust its path and behavior in response to newly detected obstacles, changed spatial configurations, and emerging tasks. This is achieved through iterative perception-action cycles that refine the robot's understanding and response to its environment.
Solution Approach 2:
The patent employs feedback mechanisms where sensor data from cameras, LIDAR, and other detectors continuously informs the robot's state estimation and decision-making processes. The system uses feedback loops to compare expected sensor readings with actual observations, detect discrepancies, and adjust navigation and task execution accordingly. This enables reliable adaptation by maintaining an accurate internal model despite environmental changes.
2Adaptability or versatility
If simple object recognition is implemented, then the robot can identify basic objects, but it cannot effectively interact with complex environments and perform diverse tasks
Solution Approach 1:
The patent implements a unified perception-action framework that handles multiple task types through a single integrated system. The same object recognition and spatial understanding mechanisms that identify objects also support navigation, manipulation planning, and interaction decision-making. This multi-functional approach allows the robot to perform diverse tasks (navigation, object manipulation, spatial reasoning) without requiring separate specialized systems for each function, thereby managing complexity while enhancing versatility.
Solution Approach 2:
The patent introduces an intermediate representation layer that bridges raw sensor data and high-level task execution. This intermediate layer includes structured models of objects, scenes, and spatial relationships that serve as mediators between perception and action. By transforming diverse sensor inputs into standardized internal representations, the system can efficiently process complex environmental information and generate appropriate task-specific actions without directly managing the full complexity of raw data for each task.
3Productivity
If the robot uses pre-programmed navigation paths, then it can move efficiently in static environments, but it cannot respond to new obstacles or changes in the workspace
Solution Approach 1:
The patent employs preliminary path planning that generates initial navigation routes based on prior knowledge of the environment. These pre-computed paths provide efficient guidance in static conditions, establishing a baseline for productive navigation. The system then layers real-time adaptation on top of this preliminary planning, allowing the robot to efficiently follow pre-planned routes when conditions permit while maintaining the capability to deviate and re-plan when dynamic changes require it.
Solution Approach 2:
The patent implements dynamic path re-planning that continuously adjusts navigation trajectories based on current sensor data and detected environmental changes. Rather than rigidly following static pre-programmed paths, the system dynamically modifies routes in response to newly detected obstacles, changed spatial configurations, or emerging tasks. This dynamic approach maintains navigation efficiency by building on preliminary plans while adapting to real-time conditions.
Data Source
AI summary
Provided is a robot, including: a chassis; a set of wheels coupled to the chassis; a processor; and a tangible, non-transitory, machine-readable medium storing instructions that when executed by the processor effectuate operations including: capturing, by an image sensor disposed on a robot, images of a workspace; obtaining, by the processor of the robot or via the cloud, the captured images; comparing, by the processor of the robot or via the cloud, at least one object from the captured images to objects in an object dictionary; identifying, by the processor of the robot or via the cloud, a class to which the at least one object belongs using an object classification unit; and instructing, by the processor of the robot, the robot to execute at least one action based on the object class identified.