Robot Guidance Using Monocular Sensor Fusion for Obstacle Replanning
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Solution Overview
Problem
Current autonomous robot systems face challenges in providing fast, accurate, and reliable positional awareness, especially in complex environments with changing conditions, as traditional methods like RFID, WiFi, and visual approaches are expensive, inaccurate, or power-intensive, and fail to incorporate real-time sensory data for task planning.
Innovation Solution
A monocular-auxiliary sensor system using a single operational camera with inertial measurement units and wheel odometry data for real-time positional awareness, enabling efficient processing of visual data and integrating sensory information for task planning and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RFID/WiFi approaches are used for positional awareness, then coverage area is extended, but cost increases and accuracy is limited
Solution Approach 1:
The patent replaces traditional RFID/WiFi electronic positioning systems with a visual-mechanical positioning system using a monocular camera and auxiliary sensors. This substitution achieves higher accuracy without proportionally increasing cost, as the camera-based visual odometry approach uses readily available consumer-grade components combined with intelligent sensor fusion algorithms.
Solution Approach 2:
The system changes the operational parameters by using a single camera operating in monocular mode with auxiliary sensors operating at different frequencies and resolutions. This parameter configuration optimizes the balance between accuracy, cost, and computational efficiency, avoiding the need for expensive multi-sensor redundant systems.
2Reliability
If depth sensor based approaches are used, then positional accuracy is improved, but power consumption increases and interference issues occur
Solution Approach 1:
Instead of using a full depth sensor system continuously, the patent employs a monocular camera that captures partial depth information selectively when needed, combined with auxiliary sensors that operate at lower power. The system activates high-precision sensing only during critical moments, reducing overall power consumption while maintaining accuracy when required.
Solution Approach 2:
The patent introduces auxiliary sensors (accelerometer, gyroscope, magnetometer) as intermediary components that bridge the gap between low-power monocular vision and high-precision depth sensing. These intermediaries provide complementary data that reduces the need for continuous high-power depth sensor operation, thereby lowering overall power consumption.
3Ease of manufacture
If visual approaches are used for fast motion applications, then cost is reduced, but processing speed becomes insufficient leading to failure
Solution Approach 1:
The patent segments the sensing and processing functions across multiple components: monocular camera for visual data, auxiliary sensors for motion data, and separate processing pipelines for each data type. This segmentation allows parallel processing of multiple data streams, increasing overall processing speed while using cost-effective individual components.
Solution Approach 2:
The system merges data from the monocular camera with auxiliary sensors (accelerometer, gyroscope, magnetometer) through sensor fusion algorithms. This combination compensates for the limitations of each individual sensor, enabling the low-cost visual approach to achieve processing speeds sufficient for fast motion applications by integrating multiple complementary data sources.
4Ease of manufacture
If traditional visual approaches are used, then cost is reduced, but scale ambiguity occurs reducing accuracy
Solution Approach 1:
The patent introduces auxiliary sensors (accelerometer, gyroscope, magnetometer) as intermediary devices that provide absolute reference frames and motion constraints. These intermediaries resolve the scale ambiguity inherent in monocular vision by providing independent measurements of acceleration, rotation, and magnetic field orientation, enabling accurate scale recovery without additional cost.
Solution Approach 2:
The system replaces active depth sensing mechanisms with a passive visual system augmented by mechanical inertial sensors. This substitution eliminates the scale ambiguity problem of pure visual approaches while maintaining low cost, as the auxiliary sensors provide the necessary reference information through passive mechanical measurements rather than active electromagnetic sensing.
5Adaptability or versatility
If real-time sensory data integration is implemented, then task planning capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a unified sensor fusion framework that processes data from the monocular camera and auxiliary sensors for multiple purposes: positional awareness, obstacle detection, and task planning. This multi-functional approach avoids the need for separate dedicated systems for each function, reducing overall complexity while enhancing adaptability and task planning capability.
Data Source
AI summary
The described positional awareness techniques employing sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to find new area to cover by a robot encountering an unexpected obstacle traversing an area in which the robot is performing an area coverage task. The sensory data are gathered from an operational camera and one or more auxiliary sensors.