Robot Blind Spot Detection Using Virtual Sensor Mapping
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Solution Overview
Problem
Robots operating in environments with obstacles often create safety risks due to blind spots, where they cannot observe humans or objects using their sensors, leading to potential collisions.
Innovation Solution
A robotic system that uses sensor units, a non-transitory computer-readable storage medium with specialized instructions, and a processor to detect blind spots by projecting virtual robots and measuring intersections on a computer-readable map, allowing the robot to perform safety actions such as emitting auditory noises or reducing speed when approaching a blind spot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots use sensors to observe their environment, then they can detect objects and navigate, but obstacles create blind spots where robots cannot observe humans or objects, leading to safety risks
Solution Approach 1:
The system performs preliminary action by detecting blind spots before the robot enters them. The processor identifies regions beyond obstacles where sensors cannot observe, and prevents the robot from navigating into these unsafe zones by adjusting the route in advance, thereby eliminating safety risks before they occur.
Solution Approach 2:
The system introduces an intermediary computational layer between the robot's navigation and the physical environment. The processor acts as a mediator that processes sensor data, identifies blind spots through geometric analysis of obstacle positions, and generates safe navigation routes, thereby compensating for the sensors' limited observation coverage.
2Productivity
If robots navigate quickly to improve productivity, then output increases, but the risk of collisions with undetected humans or objects in blind spots increases
Solution Approach 1:
The system performs preliminary safety analysis by identifying blind spots before the robot begins navigation. By pre-calculating safe routes that avoid blind spots, the robot can maintain high speeds without risking collisions, thus preserving both productivity and safety simultaneously.
Solution Approach 2:
The system implements feedback by continuously monitoring the robot's position relative to detected blind spots and adjusting the navigation route accordingly. This real-time feedback mechanism ensures that the robot maintains safe speeds and trajectories, preventing collisions while optimizing productivity through efficient path planning.
3Loss of information
If robots expand sensor coverage to eliminate blind spots, then observation completeness improves, but device complexity and cost increase
Solution Approach 1:
Instead of physically expanding sensor coverage with additional expensive sensors, the system creates a computational copy or representation of blind spots using geometric analysis of existing sensor data and obstacle positions. This virtual modeling approach achieves complete observation awareness without the complexity and cost of additional physical sensors.
Solution Approach 2:
The system replaces the mechanical approach of adding more physical sensors with a computational method. The processor uses algorithms to analyze existing sensor data, model obstacle geometries, and identify blind spots through mathematical computation, thereby substituting a complex mechanical sensor expansion with a simpler software-based solution.
Data Source
AI summary
Systems and methods for detecting blind spots using a robotic apparatus are disclosed herein. According to at least one exemplary embodiment, a robot may utilize a plurality of virtual robots or representations to determine intersection points between extended measurements from the robot and virtual measurements from a respective one of the virtual robot or representation to determine blind spots. The robot may additionally consider locations of the blind spots while navigating a route to enhance safety, wherein the robot may perform an action to alert nearby humans upon navigating near a blind spot along the route.