Autonomous Robot Velocity Costmaps for Moving Obstacle Avoidance
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Solution Overview
Problem
Existing autonomous robots rely on static costmaps that do not convey the movement of obstacles, making it difficult to dynamically adjust navigation routes for fast-moving objects, leading to insufficient collision avoidance in dynamic environments.
Innovation Solution
The generation of motion-based velocity costmaps that represent the current position, velocity, and direction of obstacles, using a two-stage approach with coarse and fine resolution costmaps to reduce processing requirements and improve real-time collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static costmaps are used for obstacle detection, then the system complexity is low, but the collision avoidance capability in dynamic environments is insufficient
Solution Approach 1:
The patent transforms the static costmap into a dynamic velocity costmap that incorporates obstacle motion information. The system calculates velocity vectors for detected obstacles and updates the costmap in real-time to reflect moving objects, enabling the robot to adapt its navigation strategy dynamically based on obstacle movement patterns.
Solution Approach 2:
The system performs preliminary velocity calculation and motion prediction for obstacles before the robot makes navigation decisions. By pre-computing velocity costmaps and predicting obstacle trajectories, the system prepares collision avoidance data in advance, allowing faster response times when navigating dynamic environments.
2Measurement precision
If high-resolution velocity costmaps are generated for all regions, then the measurement precision of obstacle velocity is high, but the processing time increases
Solution Approach 1:
The patent divides the environment into multiple regions and generates velocity costmaps selectively for each region based on obstacle presence and motion characteristics. High-resolution velocity calculations are applied only to regions containing moving obstacles, while static regions use lower-resolution representations, significantly reducing overall processing time while maintaining precision where needed.
Solution Approach 2:
The system applies different levels of velocity costmap resolution to different spatial regions based on local requirements. Regions with fast-moving obstacles receive high-precision velocity analysis, while regions with stationary or slow-moving objects use coarser resolution, optimizing the balance between measurement precision and processing efficiency.
Data Source
AI summary
Autonomous robots and methods of operating the same are disclosed. An autonomous robot includes a sensor and memory including machine readable instructions. The autonomous robot further includes at least one processor to execute the instructions to generate a velocity costmap associated with an environment in which the robot is located. The processor generates the velocity costmap based on a source image captured by the sensor. The velocity costmap includes velocity information indicative of movement of an obstacle detected in the environment.