Autonomous Motion Planning via Convex Space Decomposition
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Solution Overview
Problem
Autonomous motion planning systems face challenges in efficiently generating smooth paths while avoiding obstacles, as existing methods either result in sub-optimal paths with awkward movements or require excessive computational resources due to their non-convex and discontinuous nature.
Innovation Solution
The system decomposes the environment into convex free-space segments and occupied-space segments, using a directed acyclic graph to determine optimal path segments that connect smoothly, reducing computational workload and avoiding obstacles effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complete algorithms are used to solve autonomous motion planning, then path optimality is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent applies segmentation by decomposing the continuous motion planning problem into discrete configuration space cells. The environment is partitioned into free space cells and obstacle cells, transforming the complex continuous optimization problem into a discrete graph search problem that can be solved efficiently without exponential computational complexity.
Solution Approach 2:
The patent introduces an intermediary configuration space (C-space) that mediates between the physical environment and the path planning algorithm. By mapping the robot and obstacles into configuration space, the system creates an intermediate representation that simplifies the planning problem, allowing optimal paths to be found without directly solving the exponentially complex continuous problem.
2Productivity
If specialized algorithms are used to reduce computational complexity, then processing speed is improved, but path smoothness deteriorates with awkward movements
Solution Approach 1:
The patent applies dynamics by using continuous cost functions that are minimized along the path segments. Instead of using discrete, static path points, the system employs dynamic optimization where cost functions are continuously evaluated and minimized, ensuring smooth transitions and eliminating awkward movements while maintaining computational efficiency.
Solution Approach 2:
The patent changes parameters by transitioning from discrete path planning to continuous parameter optimization. By using continuous cost functions with parameters for path geometry and applying minimization techniques, the system achieves smooth paths that avoid awkward movements while maintaining fast processing through efficient gradient-based optimization.
3Device complexity
If discrete sampling methods are used for path planning, then computational load is reduced, but measurement precision of path quality deteriorates
Solution Approach 1:
The patent applies parameter changes by transitioning from discrete sampling to continuous parameter optimization. The system uses continuous cost functions with adjustable parameters that can be optimized to any desired precision, eliminating the quality limitations inherent in discrete sampling methods while maintaining computational efficiency through smart optimization strategies.
Solution Approach 2:
The patent applies partial action by using selective discretization only where necessary (in the configuration space decomposition) while maintaining continuity in the path optimization phase. This hybrid approach achieves the computational benefits of discretization without sacrificing the precision of continuous optimization, applying each method only where it provides the most value.
Data Source
AI summary
A motion planning system includes: a processor; and memory to store instructions that when executed by the processor, cause the processor to: identify a reference path between a departure point and a destination point in an environment including one or more obstacles; generate decomposition segments of a space surrounding the reference path, the decomposition segments including a first free-space segment and a second free-space segment that are devoid of the obstacles; generating a first path segment relative to the reference path for traversing the first free-space segment, and a second path segment relative to the reference path for traversing the second free-space segment; and connecting the first and second path segments to each other to generate a navigational path to traverse the environment.