Robot Perception Trajectory Learning for Faster Navigation Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic devices face challenges in efficiently controlling their perception systems to navigate complex environments and prioritize sensor processes, leading to inefficient movement and user experience.
Innovation Solution
A data-driven approach using a machine learning model trained on operator-directed perception system trajectories to determine optimal paths for the robotic device's perception system, incorporating planner and tracker states to enhance control and environmental awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional control methods are used for perception systems, then device complexity is reduced, but navigation efficiency and response time deteriorate
Solution Approach 1:
The patent replaces traditional mechanical control systems with a machine learning-based perception trajectory determination system. The machine learning model processes robot planner states and operator-directed perception system trajectories to generate optimized perception trajectories, substituting complex mechanical control logic with intelligent algorithms that improve navigation efficiency while managing system complexity through software-based solutions.
2Ease of operation
If operator-directed perception system trajectories are used for training, then user experience improves, but loss of time in data collection increases
Solution Approach 1:
The patent applies preliminary action by collecting operator-directed perception system trajectories during normal operation and using them to pre-train the machine learning model. This preparation phase enables the model to make accurate predictions during actual navigation, improving user experience while the training occurs in advance rather than in real-time, thus minimizing time loss during operational phases.
3Loss of information
If the perception system moves frequently to maintain awareness, then environmental awareness improves, but energy consumption increases
Solution Approach 1:
The patent changes the parameter of perception system movement from frequent random adjustments to optimized trajectories generated by the machine learning model. The model determines optimal perception trajectories based on robot planner states, allowing the perception system to maintain environmental awareness by moving strategically rather than frequently, thus reducing energy consumption while preserving information quality.
Data Source
AI summary
A method includes determining, for a robotic device that comprises a perception system, a robot planner state representing at least one future path for the robotic device in an environment. The method also includes determining a perception system trajectory by inputting at least the robot planner state into a machine learning model trained based on training data comprising at least a plurality of robot planner states corresponding to a plurality of operator-directed perception system trajectories. The method further includes controlling, by the robotic device, the perception system to move through the determined perception system trajectory.