Robot Trajectory Planning Using Weighted Similarity Selection
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Solution Overview
Problem
Existing methods for selecting potential trajectories in autonomous vehicles rely on random selection, which can lead to inefficient planning and increased runtime, and may overlook diverse or hazardous scenarios, compromising safety and robustness.
Innovation Solution
A method utilizing machine learning models to ascertain and optimize a weighted set of potential trajectories based on similarity metrics, ensuring diversity and reducing error, thereby enhancing planning efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random selection of potential trajectories is used, then the selection process is simple, but planning efficiency is low and runtime is increased
Solution Approach 1:
The patent transforms the trajectory selection problem from random sampling to an optimization problem by changing the selection parameter from uniform distribution to a metric-based weighting system. The planner selects trajectories based on computed similarity metrics and diversity measures, fundamentally changing how trajectories are chosen to improve efficiency while reducing runtime.
Solution Approach 2:
The patent replaces the mechanical random selection process with an intelligent optimization system that uses computed metrics. Instead of relying on stochastic processes, the system deterministically selects trajectories that maximize diversity and minimize error, substituting random mechanical selection with calculated optimization.
2Reliability
If random selection of potential trajectories is used, then the method is computationally simple, but safety and robustness are compromised due to overlooking diverse or hazardous scenarios
Solution Approach 1:
The patent implements feedback by computing similarity metrics between candidate trajectories and then using this feedback to guide the selection process. The system continuously evaluates trajectory diversity and adjusts selections to ensure comprehensive coverage of potential scenarios, including hazardous ones, thereby improving safety and robustness.
Solution Approach 2:
The patent performs preliminary computation of similarity metrics and diversity measures before final trajectory selection. By pre-calculating these parameters for all candidate trajectories, the system ensures that diverse and hazardous scenarios are identified and considered in advance, improving reliability without compromising the method during execution.
3Reliability
If all potential trajectories are processed for planning, then comprehensive coverage is achieved, but computational load and runtime increase significantly
Solution Approach 1:
The patent extracts and processes only the most relevant trajectories by computing similarity metrics and selecting those that maximize diversity. Instead of processing all potential trajectories, the system extracts a representative subset that maintains comprehensive coverage while significantly reducing computational load and improving efficiency.
Solution Approach 2:
The patent applies partial action by processing a carefully selected subset of trajectories rather than all possible trajectories. The similarity metric-based selection ensures that the processed subset provides sufficient comprehensiveness for safety-critical applications while avoiding the excessive computational burden of processing every potential trajectory.
Data Source
AI summary
A method for controlling a robot device. The method includes: ascertaining potential trajectories of an object in the surrounding area of the robot device by ascertaining in each case one or more potential trajectories using one or more machine learning models and, for each of them, a weighting factor assigned thereto; ascertaining weighted similarity values by ascertaining a weighted similarity value for each of the potential trajectories, including: ascertaining a similarity value that represents a similarity between the potential trajectory and a set of proposed trajectories according to at least one similarity metric, and ascertaining the weighted similarity value by weighting the similarity value according to the weighting factor assigned to the potential trajectory; adapting the set of proposed trajectories to ascertain an adapted set of proposed trajectories that results in a reduced sum of the plurality of weighted similarity values.