Robot Obstacle Avoidance with Voxel Maps and RMP Dynamics
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Solution Overview
Problem
Conventional robot obstacle avoidance systems face limitations in processing point cloud data efficiently, leading to reduced movement speed and inability to perform real-time obstacle avoidance, especially when dealing with dynamic obstacles, often getting stuck in local minima due to insufficient agility.
Innovation Solution
Employ a depth camera perception system to pre-store voxel block positions of static obstacles and monitor dynamic obstacles in real-time, using a Riemannian Motion Policies (RMP) framework to enhance the robot's agility and real-time reactive obstacle avoidance by constructing an RMP task mapping tree and performing geometric dynamic system calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the artificial potential field method is used to process point cloud data for obstacle avoidance, then the robot can avoid obstacles, but the computation cycle is extended and real-time obstacle avoidance is impossible
Solution Approach 1:
The patent segments the obstacle avoidance task into two parts: static environmental obstacles are pre-processed and stored as voxel position information, while dynamic obstacles are handled in real-time using RMP flow field guidance. This segmentation allows different processing strategies for different obstacle types, reducing overall computation time.
Solution Approach 2:
The patent performs preliminary processing of static environmental obstacles by converting point cloud data to voxel position information in advance and storing it in a map. This preliminary action eliminates the need for real-time processing of static obstacles, significantly reducing computation cycle during robot operation.
2Reliability
If the artificial potential field method is used for obstacle avoidance, then the robot can navigate around obstacles, but it gets stuck in local minima due to insufficient dynamic agility
Solution Approach 1:
The patent replaces the static artificial potential field method with a dynamic RMP (Riemannian Motion Policies) flow field guidance method. The RMP framework continuously adapts the robot's motion planning based on real-time sensor feedback and environmental changes, enabling the robot to dynamically adjust its trajectory and avoid getting stuck in local minima.
Solution Approach 2:
The patent implements continuous feedback loops where the robot's perception system continuously monitors the environment, and the RMP controller adjusts the motion planning in real-time based on this feedback. This feedback mechanism enables the system to respond to dynamic obstacles and environmental changes, preventing the robot from getting trapped in local minima.
3Loss of information
If point cloud data processing is used for all obstacles, then comprehensive obstacle information is obtained, but the processing efficiency is reduced and movement speed decreases
Solution Approach 1:
The patent applies different processing qualities to different obstacle types: static environmental obstacles are processed with high precision using voxel-based representation, while dynamic obstacles use a simplified RMP flow field approach. This local quality differentiation ensures that computational resources are focused where they are most needed, maintaining information completeness for static obstacles while improving overall processing efficiency.
Data Source
AI summary
An obstacle avoidance control method for a robot includes: obtaining, by a depth camera perception system, envelope motion information of each movable obstacle in a target operating environment in a current control cycle, and accessing a pre-stored actual voxel block position of each environmental obstacle in the target operating environment, wherein the actual voxel block position of each environmental obstacle was measured by the depth camera perception system before the movable obstacles were present in the target operating environment; and determining a number of target obstacles that need to be avoided by a number of key obstacle-avoidance parts of a target robot in the target operating environment according to all of the envelope motion information and all of the actual voxel block positions.