Probabilistic Navigation Minimizing Collision Risks
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Solution Overview
Problem
Current navigation technologies inadequately address dynamic obstacles and hazards, as they treat environmental objects as rigid entities without considering their dynamic nature, leading to inaccurate navigation and safety concerns.
Innovation Solution
A probabilistic navigation method that uses probability models to predict collision risks with static and dynamic objects, incorporating global and local factors to determine optimal routes, speeds, and directions for vehicles, thereby minimizing collision probabilities while satisfying additional constraints such as time and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a rigid no-touch zone approach is used to avoid objects, then the navigation path is simple to compute, but the navigation accuracy and safety deteriorate when objects are dynamic or hazardous
Solution Approach 1:
The patent transforms the static rigid no-touch zone into a dynamic probability field that adapts to object characteristics. Each object generates a probability field that decays with distance, and the navigation system dynamically computes paths based on minimizing expected collision cost rather than avoiding fixed zones. This allows the system to account for object dynamics (like falling trees) while maintaining computational tractability through gradient-based optimization.
Solution Approach 2:
The patent changes the parameter representation from binary (safe/unsafe zones) to continuous probability values. The collision probability P(c|s,a) is computed as a continuous field that varies with state and action, allowing nuanced risk assessment. The navigation policy optimizes over expected collision cost rather than hard constraints, enabling trade-offs between safety and other objectives like trip time or energy consumption.
2Measurement precision
If probability models are updated with multiple global and local factors, then the collision probability accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the environmental factors into global factors (affecting all objects, like weather) and local factors (affecting specific objects or regions). This segmentation allows the probability model to be updated efficiently by applying global scaling factors first, then adding local adjustments only where needed. The modular structure reduces redundant computations while maintaining comprehensive risk assessment.
Solution Approach 2:
The patent implements selective updates of probability models based on relevance. Not all factors are updated for every object at every time step - only those factors that are locally relevant are applied. This partial action approach maintains high accuracy where needed while reducing overall computational burden by avoiding unnecessary updates in low-risk regions.
3Reliability
If the navigation minimizes collision probability primarily, then the safety improves, but the trip time and energy consumption may increase
Solution Approach 1:
The patent formulates navigation as a multi-objective optimization problem where the policy minimizes a composite cost function combining expected collision cost with other objectives like trip time and energy consumption. By changing the parameter representation to expected costs rather than hard constraints, the system can find optimal trade-offs. The gradient-based optimization efficiently balances competing objectives by computing gradients of the composite cost function with respect to the navigation policy.
Data Source
AI summary
Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.