Non-Holonomic Motion Planning Graphs With Maneuver Transition Costs
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Solution Overview
Problem
Traditional motion planning methods fail to effectively address non-holonomic constraints, leading to inefficiencies in path planning for autonomous systems, especially in dynamic environments, as they do not adequately account for the limited motion capabilities of objects like vehicles and robots.
Innovation Solution
The use of graphs that discretize the solution space, incorporating vertices and edges representing machine configurations and maneuver types, along with transition costs to discourage frequent switching between maneuver types, allowing for more efficient path planning under non-holonomic constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graph-based motion planning methods are used, then the approach is simple and intuitive, but it fails to account for non-holonomic constraints and produces inaccurate paths for vehicles and robots
Solution Approach 1:
The configuration space is segmented into discrete vertices representing specific machine configurations (position, orientation, maneuver type). This segmentation allows the graph to explicitly represent non-holonomic constraints by only including valid configurations and transitions, thereby improving motion planning accuracy while managing complexity through structured discretization.
Solution Approach 2:
The patent adds a maneuver type dimension to the traditional configuration space, creating vertices that include not only position and orientation but also the current maneuver type. This dimensional extension allows the graph to capture non-holonomic constraints more accurately by representing the discrete nature of vehicle/robot motion capabilities.
2Measurement precision
If the graph includes all possible vertices and edges to accurately represent non-holonomic constraints, then motion planning accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The graph structure applies local quality by having vertices represent specific maneuver types with associated cost values that are locally optimized. Each vertex stores cost information specific to transitions from that maneuver type, allowing efficient local decision-making while maintaining global path optimality. This local organization reduces computational complexity compared to evaluating all possible paths globally.
Solution Approach 2:
The patent pre-computes and stores cost values for each vertex and edge during graph construction. These preliminary cost calculations capture the non-holonomic constraints and maneuver characteristics in advance, allowing the path planning algorithm to efficiently query pre-computed information rather than recalculating constraints during path search, thereby improving computational efficiency.
3Loss of energy
If transition costs are incorporated to discourage frequent maneuver switching, then path quality and energy efficiency improve, but the complexity of cost calculation and storage increases
Solution Approach 1:
The patent introduces transition costs as a new parameter associated with edges between vertices of different maneuver types. This parameter change allows the model to penalize frequent maneuver switching, thereby reducing energy consumption. The cost structure is organized systematically with vertices representing maneuver types and edges representing transitions, making the complexity manageable through structured parameter organization.
4Ease of operation
If a regular grid discretization is used, then the configuration space is easy to represent and process, but it cannot capture the limited motion capabilities of non-holonomic systems
Solution Approach 1:
Instead of uniform grid cells, the patent uses vertices with local quality that represent specific maneuver types and configurations. Each vertex encapsulates local motion capabilities and constraints, allowing the graph to accurately represent non-holonomic systems while maintaining structured processing through consistent vertex and edge organization.
Solution Approach 2:
The graph structure is made dynamic by including maneuver type as a discrete state variable that evolves along with position and orientation. This dynamic representation allows the graph to capture the limited motion capabilities of non-holonomic systems, where certain transitions are only valid from specific maneuver states, while still being processable through standard graph algorithms.
Data Source
AI summary
Path planning may be performed under non-holonomic constraints based at least on discretizing and selectively analyzing a solution space using a graph that includes vertices corresponding to machine configurations in a configuration space, along with associated maneuver types used by the machine to traverse these configurations. The graph may include transition edges associating costs with machine transitions between maneuver types and maneuvers. One or more of the vertices may correspond to a transition state between maneuver types. In some examples, a maneuver type may be used as a transition state between maneuver types to reduce the vertices and edges of the graph. The graph may incorporate vertices and edges representing optimal maneuver types for traversing the configuration space, including longitudinally extremal and/or laterally extremal maneuvers based on machine models.