Robotic Motion Planning With Differentiable Cost Maps
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Solution Overview
Problem
Current robotic systems face challenges in accurately navigating complex dynamic environments due to computationally taxing real-time motion planning and reliance on preprogrammed routes that fail to adapt to dynamic changes, often resulting in suboptimal motion and increased collision risks.
Innovation Solution
A method for cost evaluation and motion planning that involves evaluating a robotic device's current state and environmental context, using a kernelized footprint to generate a continuous differentiable environmental cost, and performing gradient descent on a total cost function to determine minimum cost motion commands, while incorporating collision thresholds and updates based on new sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time cost evaluation and motion planning algorithms are used to determine optimal motion commands, then navigation accuracy and adaptability to dynamic environments are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent precomputes cost maps of the environment before real-time navigation. These cost maps store pre-calculated cost values for different locations and directions, allowing the robot to quickly query optimal motion commands without performing complex real-time calculations. This preliminary preparation significantly reduces computational complexity during actual navigation while maintaining high navigation accuracy through continuous differentiable cost functions.
Solution Approach 2:
The patent transforms the discrete cost evaluation problem into a continuous differentiable optimization problem by using continuous differentiable cost functions. This parameter transformation allows the use of efficient gradient-based optimization methods instead of computationally intensive discrete search algorithms, thereby reducing computational complexity while improving navigation accuracy through smooth cost landscapes.
2Loss of time
If preprogrammed routes are used for robot navigation, then computational processing time is reduced, but adaptability to dynamic changes and moving objects deteriorates
Solution Approach 1:
The patent implements dynamic motion planning by continuously updating motion commands based on current robot state and environmental context. The system uses real-time sensor data to detect moving objects and dynamically adjusts the cost map and motion planning accordingly. This dynamic approach allows the robot to adapt to changing environments while maintaining efficient processing through the use of precomputed cost structures and continuous optimization.
Solution Approach 2:
The patent incorporates feedback mechanisms where the robot continuously evaluates its current state, compares it with the target trajectory, and adjusts motion commands based on real-time environmental feedback. The system uses sensor data to detect changes in the environment and updates the cost evaluation accordingly, enabling adaptive navigation while maintaining computational efficiency through incremental updates rather than complete re-planning.
3Measurement precision
If continuous differentiable cost functions are used for motion planning, then optimal motion determination is improved, but computational requirements for real-time execution increase
Solution Approach 1:
The patent precomputes cost maps and stores them for quick retrieval during real-time navigation. This preliminary computation separates the heavy computational burden from real-time execution, allowing continuous differentiable cost functions to be used for optimal motion determination without exceeding real-time computational power constraints.
Solution Approach 2:
The patent replaces computationally intensive discrete optimization methods with continuous differentiable optimization methods that can be solved more efficiently using gradient-based algorithms. This substitution reduces the computational power required for real-time execution while maintaining or improving the precision of optimal motion determination.
Data Source
AI summary
Systems, apparatuses, and methods for cost evaluation and motion planning for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for producing and evaluating a continuous and differentiable total cost as a function of all available motion commands is disclosed and may be utilized in conjunction with a gradient descent to determine a minimum cost motion command corresponding to an optimal motion for a robotic device to execute in accordance with a target trajectory and obstacle avoidance.