Geometric Robotic Platform for Component-Failure-Resilient Terrain Navigation
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Solution Overview
Problem
Existing robotic systems lack efficient and robust mechanisms for autonomous movement and environmental exploration, particularly in harsh or hostile terrains, with limited adaptability and resilience to component failures.
Innovation Solution
A robotic system featuring a polyhedral structure with independently moveable components, each equipped with a motor and camera, allowing for coordinated movement and environmental mapping, utilizing a kinematic technique to navigate and adapt to terrain, and employing machine learning for obstacle avoidance and target tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robotic systems are used, then they can perform basic tasks, but they lack robustness and adaptability in harsh terrains and to component failures
Solution Approach 1:
The robotic system is divided into multiple independently controllable components or modules, each capable of autonomous operation. This segmentation allows the robot to maintain functionality even when individual components fail, improving reliability while enabling adaptation to complex terrains through coordinated module behavior
Solution Approach 2:
The robotic system employs dynamic reconfiguration capabilities where components can change their operational states and roles in real-time based on terrain conditions and component status. This dynamic adaptability allows the system to optimize performance across varying environmental conditions while maintaining robustness
2Ease of operation
If complex control systems are implemented for autonomous movement, then navigation capability improves, but system complexity and computational requirements increase
Solution Approach 1:
The robotic system incorporates self-navigation and self-adjustment capabilities through distributed intelligence, where individual components can autonomously sense their environment and make local decisions. This reduces the need for complex centralized control while maintaining effective autonomous operation
Solution Approach 2:
The system implements continuous feedback loops between sensors, processors, and actuators that enable real-time adaptation to terrain conditions. This feedback mechanism simplifies control by allowing the system to automatically adjust its behavior based on environmental input rather than requiring complex pre-programmed responses
3Adaptability or versatility
If multiple sensors and cameras are added for environmental mapping, then exploration capability improves, but energy consumption and device complexity increase
Solution Approach 1:
The robotic system uses multi-functional sensors that can perform multiple tasks simultaneously, such as cameras that both navigate and map the environment, or sensors that detect both obstacles and terrain characteristics. This reduces the total number of components needed while maintaining comprehensive environmental awareness
Solution Approach 2:
The system implements selective sensing that activates only the necessary sensors and processing capabilities for current operational needs. This partial action approach reduces energy consumption by avoiding continuous full-system operation while maintaining adequate environmental mapping capability
Data Source
AI summary
Various aspects of methods, systems, and use cases include a robotic system and control or use of the robotic system. A method for autonomous movement of the robotic system may include receiving a target location, identifying robotic components of the robotic system that are in contact with a surface, and determining a robotic component of the robotic components to activate to cause the robotic system to move closer to the target location. The method may include activating, based on the determination, a motor of the robotic component to push against the surface to cause the robotic system to move towards the target location.