Robot Motion Planning Using Multi-Resolution Swept Volumes
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Solution Overview
Problem
Current motion planning systems for robots face challenges in efficiently planning paths for various environments and tasks at low costs and high speeds, particularly in complex settings with obstacles and dynamic objects.
Innovation Solution
A robotic system equipped with a motion planning processor and appendages that use sensor data to generate and execute motion plans, allowing the robot to navigate through environments without collisions by determining optimal poses and orientations for grasping and positioning objects in diverse tasks such as assembly, inspection, and packaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional motion planning methods are used for complex environments with obstacles and dynamic objects, then the robot can navigate safely, but the computation time increases and speed decreases
Solution Approach 1:
The motion planning problem is divided into multiple sub-problems by segmenting the configuration space into discrete cells or regions. This allows the planner to process each segment independently and efficiently, reducing overall computation time while maintaining safety guarantees through systematic exploration of all segments.
Solution Approach 2:
The system performs preliminary actions by pre-processing environment data, pre-computing potential paths, or pre-identifying obstacles before actual motion execution. This preparation work reduces real-time computation requirements, enabling faster response speeds while maintaining reliable collision-free navigation through advance planning.
2Adaptability or versatility
If complex motion planning algorithms are implemented to handle various environments and tasks, then the robot's adaptability improves, but the system complexity and computational cost increase
Solution Approach 1:
The motion planning system is designed with universal components and data structures that can handle multiple environment types and task categories through a unified framework. This multi-functionality allows the same core algorithm to adapt to various scenarios (static obstacles, dynamic objects, different workspace geometries) without requiring separate specialized systems, thus improving adaptability while controlling complexity.
Solution Approach 2:
The planning system incorporates dynamic elements that allow it to adapt its behavior based on environmental conditions and task requirements. This includes dynamic re-planning capabilities, adjustable search parameters, and flexible data structures that can accommodate different scenario complexities, enabling high adaptability through a single flexible system rather than multiple rigid specialized systems.
3Measurement precision
If detailed environmental data is processed to ensure safe navigation, then collision detection accuracy improves, but data processing time increases
Solution Approach 1:
The system applies partial processing to environmental data by focusing computational resources on critical regions or aspects most relevant to immediate navigation decisions. Rather than processing all available data with equal detail, it selectively processes portions that have the greatest impact on collision detection accuracy, thus maintaining high precision for safety-critical measurements while reducing overall processing time through selective attention to essential data elements.
Data Source
AI summary
A robot control system determines which of a number of discretizations to use to generate discretized representations of robot swept volumes and to generate discretized representations of the environment in which the robot will operate. Obstacle voxels (or boxes) representing the environment and obstacles therein are streamed into the processor and stored in on-chip environment memory. At runtime, the robot control system may dynamically switch between multiple motion planning graphs stored in off-chip or on-chip memory. The dynamically switching between multiple motion planning graphs at runtime enables the robot to perform motion planning at a relatively low cost as characteristics of the robot itself change. Various aspects of such robot motion planning are implemented in particular systems and methods that facilitate motion planning of the robot for various environments and tasks.