Mobile Manipulator Navigation with Dynamic State-Space Switching
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Solution Overview
Problem
Existing mobile manipulator platforms face challenges in autonomous navigation due to high-dimensional mobility spaces, where the separation of autonomy algorithms and mechatronic design complicates simultaneous movement and manipulation, leading to inefficiencies and inability to reposition the arm or leverage inertial energy efficiently.
Innovation Solution
An autonomous navigation system with a motion planning algorithm that dynamically adjusts the state search space, incorporating a Hybrid Ground Autonomous Manipulator Vehicle (HGAMV) capable of transforming between fixed and mobile states, using sensors for environment mapping and replanning supervisor to optimize trajectory planning by enabling or disabling degrees of freedom based on a cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single motion algorithm is used for high-dimensional mobility space, then the system structure is simplified, but the computational complexity and time required for navigation planning increase significantly
Solution Approach 1:
The patent segments the high-dimensional motion planning problem into two separate lower-dimensional planning problems: one for the mobile base (position and orientation) and one for the robotic arm (end-effector position and orientation). This segmentation reduces the computational complexity from planning in a combined high-dimensional space to planning in two separate lower-dimensional spaces, thereby reducing computational time while maintaining system structure simplicity.
2Device complexity
If the mobile base and robotic arm are handled sequentially, then the control algorithm is simplified, but the ability to optimize movement efficiency and repositioning is reduced
Solution Approach 1:
The patent merges the mobile base control and robotic arm control into a unified task-space control framework. Instead of handling them sequentially with separate algorithms, the system combines both controllers to work simultaneously toward a common task goal. This merging enables coordinated optimization where the mobile base can reposition to improve arm accessibility while the arm performs manipulation tasks, thereby enhancing movement efficiency without significantly increasing control algorithm complexity.
3Device complexity
If the robot maintains fixed platform configuration, then the control system is simpler, but the adaptability to different task requirements and energy efficiency is reduced
Solution Approach 1:
The patent implements a dynamic platform configuration system where the mobile base can switch between mobile and fixed configurations based on task requirements. The control system dynamically adjusts the platform's degrees of freedom: when the arm needs to reach distant targets, the base moves to reposition; when the arm needs stable support for precision tasks, the base locks into a fixed configuration. This dynamic adaptability enhances task versatility while keeping the control system relatively simple through standardized control interfaces.
Data Source
AI summary
An apparatus, including: an interface operable to receive sensor data and generate a map representation of an environment of a robot; processing circuitry operable to: plan a sequence of states to direct the robot to a task goal of the robot based on the map representation and a kinematic state of the robot for a plurality of degrees of freedom; determine a time-dependent trajectory of the robot to the task goal based on the sequence of states by dynamically enabling or disabling one or more of the plurality of degrees of freedom; and generate a movement instruction to control movement of the robot based on the time-dependent trajectory.