Compressed Motion Planning Indices for Real-Time Collision Checks
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Solution Overview
Problem
Current motion planning algorithms for autonomous agents face computational bottlenecks in real-time collision checking and decision-making, particularly when evaluating numerous candidate motions against environmental predicates, leading to inefficiencies in path selection and obstacle reaction.
Innovation Solution
The method involves generating and compressing indices representing predicates and candidate motions using locality-preserving curves like Hilbert curves and run-length encoding, allowing for efficient intersection evaluation directly in a compressed format, reducing the number of operations required for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If massively parallel evaluation of a large library of pre-computed motions is performed, then decision-making accuracy is improved, but computational time and processing complexity increase
Solution Approach 1:
The patent pre-computes and stores a library of candidate motions before real-time operation. During runtime, the system only needs to evaluate and select from these pre-prepared motions by comparing their indices with the environmental predicate, dramatically reducing real-time computational requirements while maintaining decision accuracy.
Solution Approach 2:
The patent extracts only the essential intersection evaluation between motion indices and environmental predicates, separating this critical operation from the full motion simulation and rendering pipeline. This extraction allows the system to focus computational resources solely on the decision-critical intersection check.
2Reliability
If a large library of candidate motions is maintained for comprehensive evaluation, then decision-making reliability is improved, but memory requirements and data processing complexity increase
Solution Approach 1:
The patent transforms the representation of candidate motions from detailed geometric models to compressed index structures that encode spatial occupancy information. This parameter transformation reduces memory requirements and enables efficient bitwise operations for intersection evaluation, maintaining reliability while reducing complexity.
Solution Approach 2:
The patent creates simplified index representations (copies) of the environmental predicate and candidate motions that preserve the essential intersection information without requiring the full detailed models. These index copies enable rapid evaluation while the full models remain stored for reference if needed.
3Measurement precision
If detailed environmental predicates and motion trajectories are processed in real-time, then collision detection accuracy is improved, but computational operations required increase
Solution Approach 1:
The patent pre-computes the index representations of candidate motions during offline preparation, so that during real-time operation only the intersection evaluation with current environmental predicates is needed. This preliminary preparation maintains collision detection accuracy while dramatically improving processing speed.
Solution Approach 2:
The patent replaces traditional geometric intersection algorithms with index-based bitwise comparison operations. This substitution of computational mechanics enables parallel processing of multiple candidate motions simultaneously, improving processing speed while maintaining accuracy through the preservation of essential spatial information in the index structures.
Data Source
AI summary
A computer-implemented method of motion planning and an autonomous agent that executes that method are presented. The method includes generating a first set of indices that represent a predicate in a continuous state space, accessing a library of candidate motions, wherein each of the candidate motions has a second set of indices that represent a trajectory in the continuous state space, compressing the first set of indices and the second set of indices to generate a first set of compressed indices and a second set of compressed indices, and determining whether there is an intersection between the first set of indices and the second set of indices. The compressing may be done by partitioning the state space into regions, each region being occupied by the same set of candidate motions. Alternatively, the compressing may be done by applying run-length-encoding to binary vectors generated using a locality preserving, space filling curve such as a Hilbert curve or Z-order curve.