Autonomous Space Robotic Inspection with Lidar Collision Avoidance
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Solution Overview
Problem
Current robotic inspection operations in space require human involvement, which inhibits operations during loss of signal and poses risks to personnel and equipment, and there is a need for an autonomous system to detect and avoid threats.
Innovation Solution
An autonomous robotic inspection system equipped with software tools for autonomous sensing of unexpected obstacles, model-based collision avoidance, worksite survey, and vision-guided motion, enabling autonomous robotic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators perform inspection operations, then operational safety can be monitored, but operations cannot be conducted during loss of signal and personnel safety is at risk
Solution Approach 1:
The robotic system performs self-inspection using autonomous sensing tools including lidar, cameras, and other sensors to detect obstacles and threats without human intervention. The system independently processes sensor data to identify discrepancies between modeled and real environments, enabling operations during signal loss while maintaining safety monitoring
Solution Approach 2:
The patent replaces human operators with autonomous software tools and algorithms that process sensor data and make navigation decisions. Machine learning models and collision avoidance algorithms substitute for human judgment, enabling continuous autonomous operation without personnel exposure to space-based hazards
2Ease of operation
If manual inspection scheduling is used, then operational control is maintained, but operational efficiency is reduced due to ground crew involvement
Solution Approach 1:
The system performs preliminary autonomous sensing and obstacle detection before executing navigation tasks. Inspection operations are automatically scheduled and executed based on task requirements without waiting for ground crew assessment, enabling continuous efficient operation while maintaining safety through pre-execution environmental evaluation
Solution Approach 2:
The inspection system dynamically adapts its operation based on real-time sensor data and task requirements. The autonomous system can adjust inspection frequency and depth based on environmental conditions and operational context, optimizing efficiency while maintaining necessary safety monitoring without fixed manual scheduling constraints
3Reliability
If traditional collision avoidance methods are used, then simple obstacles can be avoided, but complex unexpected obstacles cannot be detected
Solution Approach 1:
The obstacle detection system segments the environment into multiple sensing zones using lidar and camera arrays. Different sensor types detect different obstacle characteristics, with lidar providing range data and cameras providing visual identification. This segmented approach enables detection of diverse obstacle types including unexpected objects that single-sensor systems would miss
Solution Approach 2:
The system continuously compares sensor data against modeled environment data, providing real-time feedback on discrepancies. When unexpected obstacles are detected, the system adjusts its navigation plan and collision avoidance parameters dynamically, improving adaptability to complex situations while maintaining reliable collision avoidance through continuous monitoring and adjustment
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
Enables safe and autonomous robotic operations by detecting obstacles, avoiding collisions, and performing inspections without human intervention, ensuring operational safety and efficiency.
Implementation Method 1
The ASUO software tool is configured to register observation data comprising lidar data and optical data to as-built models of a worksite in which the robotic device is operating
Implementation Method 2
the VGM software tool is configured to perform incremental look-and-move fine alignment maneuvers to correct pose misalignments to a fixed target computed using one or more machine vision cameras
Data Source
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AI summary
An autonomous inspection system for detecting and avoiding threats during operation of a robotic system comprising a robotic device is provided. The robotic device performs a series of robotic operations each including a path and a trajectory. The autonomous inspection system includes a memory device for storing data and a processor in communication with the memory device and for processing the data stored by the memory device. The processor is configured to execute at least one of an autonomous sensing of unexpected obstacles (ASUO) software tool, a model-based collision avoidance (MBCA) software tool, a worksite surveyor (WS) software tool, and a vision-guided motion (VGM) software tool, to detect threats during the series of robotic operations.