Predictive Robot Obstacle Detection for Tolerance-Based Picking
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Solution Overview
Problem
Autonomous robots face challenges in precision due to imprecise actuators and sensors, leading to potential collisions and damage during object retrieval and placement tasks, especially when dealing with freely organized objects without specialized storage equipment.
Innovation Solution
The implementation of predictive obstruction detection systems in robots, which use sensors to compute tolerances and detect potential obstructions before performing tasks, allowing for preemptive adjustments or task abortion to avoid collisions and ensure safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed slots or spaces with sidewalls are used in storage shelving, then robot operations reliability is improved by preventing object collisions and displacements, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical guidance system (fixed slots with sidewalls) with a sensor-based predictive detection system. The robot uses sensors to detect object positions and predicts potential collisions before they occur, allowing the robot to adjust its operations without requiring specialized storage infrastructure.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes sensor data and predicts potential collisions. This intermediary system acts as a mediator between the robot's actuators and the storage environment, enabling safe operations without physical guidance structures.
2Manufacturing precision
If fixed slots or spaces are used in storage shelving, then object positioning precision is improved, but adaptability to objects of different dimensions deteriorates
Solution Approach 1:
The patent makes the robot's operational parameters dynamic rather than static. Instead of fixed slots that constrain all objects, the robot dynamically adjusts its predicted collision boundaries based on real-time sensor detection of object positions and dimensions, enabling adaptation to various object sizes while maintaining precision.
Solution Approach 2:
The patent changes the parameters used for collision detection from fixed geometric constraints to variable parameters based on actual object positions and robot operational tolerances. This allows the system to adapt to different object dimensions while maintaining precise and safe operations.
3Reliability
If predictive obstruction detection is implemented, then robot operational safety is improved by preventing collisions, but measurement precision requirements increase
Solution Approach 1:
The patent performs preliminary detection and prediction of potential collisions before the robot executes its operational tasks. By detecting object positions and predicting future collision risks in advance, the system can prepare appropriate responses without requiring extremely high measurement precision during critical operations.
Solution Approach 2:
The patent creates a predictive buffer zone or tolerance boundary that accounts for sensor measurement uncertainties and actuator imprecisions. This beforehand cushioning allows the robot to operate safely even with moderate measurement precision by maintaining predicted safe boundaries that incorporate expected deviations.
Data Source
AI summary
Provided are systems and methods by which robots predictively detect objects that may obstruct robotic tasks prior to the robots performing those tasks. For instance, a robot may receive a task, and may obtain dimensions of a task object based on a first identifier obtained with the task or with a sensor of the robot. The robot may determine a first boundary for moving the task object based on the task object dimensions and an added buffer of space that accounts for imprecise robotic operation. The robot may detect a second identifier of a neighboring object, and may obtain dimensions of the neighboring object using the second identifier. The robot may compute a second boundary of the neighboring object based on the dimensions of the neighboring object and a position of the second identifier, and may detect an obstruction based on the second boundary crossing into the first boundary.