Constraint-Aware Task Planning for Dynamic Obstacle Navigation
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Solution Overview
Problem
Current machine learning systems face challenges in navigating dynamic environments with multiple constraints, such as avoiding static and dynamic obstacles, where existing methods struggle to efficiently combine and optimize various constraints in real-time without retraining models.
Innovation Solution
The system employs stochastic optimization with a processor that uses LiDAR data and reinforcement learning to model constraints as costs, allowing for parallel processing and independent training of constraints, enabling efficient navigation by predicting obstacle trajectories and optimizing actions in continuous space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning methods are used to navigate dynamic environments with multiple constraints, then the system can handle complex navigation tasks, but it struggles to efficiently combine and optimize various constraints in real-time without retraining models
Solution Approach 1:
The patent segments the navigation task into separate constraint handling modules, where each constraint (obstacle avoidance, goal reaching, boundary constraints) is processed independently through dedicated neural network branches. This segmentation allows each constraint to be optimized separately in real-time without requiring full model retraining, thus improving real-time optimization efficiency while maintaining the ability to handle multiple constraints.
Solution Approach 2:
The patent creates a universal optimization framework that can handle multiple different types of constraints through a single stochastic optimization process. The framework uses a unified cost function that incorporates various constraints (dynamic obstacles, static obstacles, goal attraction) that can all be optimized simultaneously without retraining, providing both adaptability to multiple constraint types and real-time optimization efficiency.
2Productivity
If constraints are modeled as costs in stochastic optimization, then parallel processing and independent training of constraints is enabled, but the system complexity increases
Solution Approach 1:
The patent segments the constraint processing into separate parallel branches, each handling a specific constraint type (dynamic obstacles, static obstacles, goal reaching). Each branch can be trained independently and processes constraints in parallel during execution. While this increases architectural complexity, it enables significant parallel processing capability that improves real-time performance.
Solution Approach 2:
The patent merges the separately processed constraints into a unified stochastic optimization framework that combines all constraint costs into a single objective function. This merging allows the system to leverage the parallel processing benefits of separate constraint handling while maintaining a cohesive decision-making process, balancing the increased architectural complexity with improved productivity.
3Loss of time
If reinforcement learning is used to provide initial action estimates, then the convergence of stochastic optimization is improved, but the training time and computational resources increase
Solution Approach 1:
The patent uses reinforcement learning to generate preliminary action estimates before the stochastic optimization process begins. These pre-computed action estimates serve as informed initial guesses that guide the optimization toward the optimal solution more quickly, reducing the number of iterations needed for convergence. This preliminary action reduces optimization time but requires additional computational resources for the reinforcement learning training phase.
Data Source
AI summary
An apparatus for performing a task, the task being a sequence of actions performed to achieve a goal, the apparatus comprising:at least one sensor for obtaining observations of the apparatus;a controller configured to receive a control signal to move said apparatus; anda processor,said processor being configured to:receive information concerning the goal;determine the sequence of actions to reach said goal, the sequence of actions being subject to at least one constraint; andprovide a control signal to said controller for the next action in said sequence of actions,wherein said processor is configured to determine the sequence of actions by processing observations received by said sensors to obtain information concerning the at least one constraint and performing stochastic optimisation to determine the sequence of actions where the at least one constraint is represented as a cost in said stochastic optimisation, the stochastic optimisation receiving an initial estimate of the next action.