Mobile Robot Local Planner Switching for Obstacle-Adaptive Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous robot control systems face challenges in dynamically switching between traditional and learning-based local planners to effectively navigate through varying obstacle conditions.
Innovation Solution
A mobile robot is configured to dynamically determine whether to use a traditional local planner or a reinforcement learning local planner based on current nearby obstacles, with the decision influenced by a local cost map and path clearance statuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional local planner is used, then the robot navigation is robust and stable, but the efficiency in challenging situations is reduced
Solution Approach 1:
The system dynamically switches between traditional and reinforcement learning local planners based on real-time environmental conditions. The robot evaluates path clearance statuses and cost map data to determine when to use each planner type, making the navigation system adaptive rather than static.
Solution Approach 2:
The system changes the operational parameters by switching between two different planner algorithms based on environmental parameters. When obstacles are detected in consecutive waypoints, the system transitions from traditional planner to reinforcement learning planner, and vice versa, optimizing performance based on current conditions.
2Productivity
If a reinforcement learning local planner is used, then the navigation efficiency in challenging situations is improved, but the robustness and stability are reduced
Solution Approach 1:
The system employs dynamic switching between planner types based on real-time environmental assessment. The robot continuously monitors path clearance statuses and transitions between traditional and reinforcement learning planners to maintain both stability and efficiency under varying conditions.
Solution Approach 2:
The system adjusts its operational mode by changing which planner algorithm is active based on environmental parameters. When the environment is clear, the traditional planner maintains stability; when obstacles are present, the reinforcement learning planner improves efficiency, thus optimizing the reliability-productivity tradeoff.
3Adaptability or versatility
If the robot dynamically switches between traditional and reinforcement learning planners, then the adaptability to varying obstacle conditions is improved, but the system complexity increases
Solution Approach 1:
The navigation system is segmented into two distinct planner modules (traditional and reinforcement learning), each handling specific scenarios. The system divides the decision-making process into discrete evaluation steps: generating waypoints, assessing path clearance, and selecting the appropriate planner based on predefined criteria.
Solution Approach 2:
The system introduces an intermediary decision-making layer that evaluates environmental conditions (path clearance statuses, cost map data) and mediates between the two planner types. This intermediary component manages the complexity by providing a structured switching mechanism based on observable environmental parameters.
Data Source
AI summary
A mobile robot includes at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the mobile robot to generate a set of next consecutive waypoints, determine a local planner based on the set of next consecutive waypoints, and output a velocity pair for navigating the mobile robot, based on the determined local planner.