Robot Contact Control Using Particle-Based Pose Estimation
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Solution Overview
Problem
Conventional robot control methods fail to accurately account for uncertainties in the relative position and posture of an end effector and a gripped object, leading to failed operations due to errors in sensor observations and uncertain target positions and postures.
Innovation Solution
A robot control device and method that utilize particle filtering to adjust weights of particles representing possible positions and postures, incorporating sensor observations to estimate and adjust actions to achieve precise contact between the operation object and the object of interest, iteratively refining the robot's movements until a predetermined error tolerance is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robot control methods are used with predetermined operations, then the control process is simple, but the position and posture of the gripped object cannot reach the target due to uncertainties in relative position and posture
Solution Approach 1:
The patent implements feedback by continuously observing the position and posture of the gripped object using sensors during robot operation. The observed values are fed back to update particle weights in the particle filter, allowing the system to adjust control actions based on actual object states rather than relying solely on predetermined operations. This feedback mechanism resolves the contradiction by improving operation reliability through adaptive control while managing complexity through probabilistic estimation.
Solution Approach 2:
The patent changes parameters by representing uncertain position and posture as a set of particles with associated weights rather than fixed values. The particle weights are dynamically adjusted based on sensor observations and contact detection, allowing the system to adapt to uncertainties in relative position and posture. This parameter transformation enables reliable operation control despite initial uncertainties, resolving the contradiction between reliability and complexity.
2Extent of automation
If camera-based recognition is used to determine position and posture, then automation is improved, but sensor errors cause uncertainty in the recognized position and posture
Solution Approach 1:
The patent applies beforehand cushioning by preparing a set of particles representing possible position and posture variations before operation begins. These particles account for potential sensor errors and uncertainties in advance. When sensor measurements are obtained, the particle weights are updated to reflect the actual state, cushioning against the impact of sensor errors on operation accuracy. This approach maintains automation while compensating for measurement precision limitations.
Solution Approach 2:
The patent introduces particles as an intermediary between sensor observations and control actions. Instead of directly using potentially erroneous camera-based recognition results, the system uses particles to represent a distribution of possible states. The particle filter acts as a mediator that processes sensor data and produces more reliable position and posture estimates, resolving the contradiction between automation and measurement precision.
3Measurement precision
If particle filtering is used to estimate position and posture, then measurement precision is improved, but the computational processing complexity increases
Solution Approach 1:
The patent applies partial action by maintaining a limited set of discrete particles rather than using continuous probability distributions. This partial representation of the state space reduces computational complexity while still capturing the essential uncertainties in position and posture. The system performs sufficient particle filtering to improve measurement precision without exhaustively exploring all possible states, resolving the contradiction between precision and computational complexity.
4Ease of operation
If the robot operates without paying attention to gripped object position and posture during control, then the control process is simple, but the operation fails to achieve the target position and posture
Solution Approach 1:
The patent implements self-service by enabling the robot to automatically observe and adjust to the actual position and posture of the gripped object during operation. The system uses sensors to detect object state and automatically updates particle weights and control actions without requiring external intervention. This self-adjusting mechanism maintains ease of operation while improving reliability by ensuring the robot responds to actual object positions rather than relying solely on predetermined control sequences.
Data Source
AI summary
The observation update unit increases a weight of a particle for a particle representing a position and a posture closer to a position and a posture of an operation object indicated by an observation result, and increases a weight of a corresponding particle for a particle closer to a state in which an object of interest and the operation object arranged in a position and a posture represented by each of particles are in contact with each other in a virtual space where shapes and a relative positional relationship of the object of interest and the operation object are expressed in a case in which the observation result indicates that the contact has occurred.