Mobile Object Path Planning With Layered Dynamic Costmaps
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Solution Overview
Problem
Current path planning for autonomous navigation systems fails to consider the dynamic changes in traffic situations and the movement patterns of multiple mobile objects, leading to suboptimal paths in complex environments.
Innovation Solution
A layered costmap approach that integrates static, dynamic, and historical obstacle and mobile object data to plan an optimal path, using ultrasonic sensors, LiDAR, cameras, and UWB systems to detect and predict the movement of other mobile objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If the global path planner plans a shortest global path based on a static map, then the path length is minimized, but the path optimality deteriorates when there are multiple stationary or dynamic obstacles
Solution Approach 1:
The costmap is transformed from a static representation to a dynamic, multi-layered structure that continuously updates obstacle information. The system maintains multiple costmap layers (static obstacles, dynamic obstacles, mobile objects with path planning modules, mobile objects without path planning modules) that are dynamically updated based on sensor data and historical information, allowing the path planner to adapt to changing environmental conditions while maintaining global path optimization.
Solution Approach 2:
The costmap is segmented into multiple independent layers, each representing different types of obstacles and information sources. This segmentation allows the system to process and weight different obstacle types separately, then integrate them into a comprehensive costmap for optimal path planning, resolving the contradiction between path length and path optimality in complex environments.
2Adaptability or versatility
If the local path planner continuously adjusts the path based on current sensor detection, then the path adaptability improves, but the global path optimality deteriorates
Solution Approach 1:
The system merges global path planning with local path adjustment by integrating multiple costmap layers that combine global static map information with local dynamic obstacle data. The master costmap synthesizes information from all layers, enabling the path planner to maintain global optimality while adapting to local changes through continuous updates from sensor data and historical information.
Solution Approach 2:
The system implements feedback mechanisms by continuously updating the costmap layers with current sensor data and historical information about mobile objects. This feedback loop allows the path planner to adjust the global path optimally based on real-time environmental changes, resolving the contradiction between global optimality and local adaptability.
3Device complexity
If the traffic flow analysis only considers current locations of mobile objects, then the system complexity is reduced, but the path planning accuracy deteriorates in larger environments with long paths
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical location data of mobile objects in the fourth costmap layer. This historical information is prepared in advance and integrated into the costmap before path planning occurs, allowing the system to account for future traffic situations at remote locations without increasing runtime complexity, thus improving path planning accuracy in large environments.
Data Source
AI summary
A method for planning a path for a mobile object is disclosed. The method includes: generating a plurality of costmap layers based at least on locations of the obstacles in a navigation area, current locations and/or current planned paths of other mobile objects in the navigation area, and historical locations of other mobile objects in the navigation area; generating a master costmap based on the plurality of costmap layers; and planning a path to a target location based on the master costmap. An apparatus for planning a path for a mobile object is also disclosed. A controller for a mobile object, a mobile object and a computer-readable storage medium are also disclosed.