Autonomous Robot Sensor Fusion for User Tracking and Obstacle Detection
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Solution Overview
Problem
Existing robotic systems lack comprehensive integration with their environment and users, struggling with adaptability, obstacle detection, and efficient human-machine interaction, particularly in diverse lighting conditions and complex scenarios.
Innovation Solution
An autonomous robotic system with modular design, incorporating multiple sensors (RGB, depth, LIDAR, sonar, and cameras) for real-time data integration from various sources, enabling flexible operation modes and advanced processing algorithms for navigation, interaction, and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor types (RGB, depth, LIDAR, sonar) are integrated for comprehensive environmental perception, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The robotic system divides environmental perception into multiple specialized sensor modules (RGB camera, depth camera, LIDAR, sonar), where each sensor type handles specific aspects of environmental detection. This segmentation allows each sensor to optimize for its specific function while collectively providing comprehensive perception coverage.
Solution Approach 2:
The patent combines multiple heterogeneous sensor types (optical, acoustic, laser-based) into a unified sensor integration module that processes data from all sources simultaneously. This merging enables the system to leverage complementary strengths of different sensors to achieve superior measurement precision and reliability.
2Adaptability or versatility
If the robot operates in autonomous mode with advanced processing algorithms for real-time decision-making, then adaptability and intelligence are improved, but use of energy increases
Solution Approach 1:
The robotic system dynamically adjusts its processing algorithm intensity and sensor activation based on operational context. In low-risk environments, the system reduces processing complexity and energy consumption, while activating advanced algorithms and full sensor suites only when environmental complexity or risk levels require enhanced adaptability.
Solution Approach 2:
The system changes operational parameters such as processing frequency, sensor sampling rates, and algorithm complexity based on real-time environmental assessment. This allows the robot to maintain high adaptability when needed while conserving energy during routine operations with predictable patterns.
3Reliability
If the system integrates real-time data from multiple information sources (sensory input, operator input, external systems), then reliability and intelligence are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces a central processing unit that acts as an intermediary between multiple information sources (sensory modules, operator interfaces, external information systems). This mediator integrates and harmonizes data from diverse sources, managing data flows and coordination while presenting unified information to control algorithms, thereby reducing overall system complexity.
4Measurement precision
If advanced obstacle detection and user tracking algorithms are implemented, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The robotic system implements tracking algorithms that focus computational resources on partially processing the entire environment, concentrating processing power on identified regions of interest such as detected users or potential obstacles. This partial action approach maintains high tracking precision while reducing overall energy consumption by avoiding exhaustive processing of all environmental data.
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
The system achieves enhanced adaptability and safety by integrating sensory data for efficient obstacle detection and user tracking, allowing autonomous operation and effective interaction in various environments, including natural and artificial light conditions.
Implementation Method 1
The robotic system is comprised by the following technical modules: sensory module, monitoring module, interaction module, central processing module, power module and locomotion module... at least one sensor with LIDAR technology
Implementation Method 2
The sensory module can be equipped as a more robust range of sensors if the robotic system is programmed for the user's tracking mode... at least one sonar (with operating frequency in the ultrasound or infrared range)
Data Source
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AI summary
The present application discloses an autonomous robotic system, arising from the need to make this type of systems more rational and 'conscious', favoring their complete integration in the environment around them. This integration is promoted through the integration of sensory data, information entered by the user, and context information sent by external agents to which the system is connected. Real-time processing of all these data, coming from different entities, endows the system with an intelligence that allows it to operate according to different operation modes, according to the function assigned thereto, allowing it to operate exclusively following its user or alternatively to move autonomously directly to a particular defined point.