Mobile Robot Preferred Pathways for Obstacle-Aware Navigation
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Solution Overview
Problem
Mobile robots face challenges in navigating through environments with unknown obstacles, leading to inefficiencies and increased manual effort in determining optimal pathways for transportation tasks.
Innovation Solution
Implementing systems and methods that use cost functions to influence path selection by mobile robots, allowing them to determine preferred pathways based on factors like obstacle avoidance and traffic conditions, and enabling remote servers to send navigation instructions during transit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mobile robots navigate autonomously through environments with unknown obstacles, then robot autonomy and operational independence are improved, but navigation reliability and path optimality deteriorate due to inability to predict unseen obstacles
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple possible pathways before the robot encounters obstacles. These pathways are prepared in advance and stored for quick reference when obstacles are detected, allowing the robot to rapidly switch to pre-planned alternative routes without compromising autonomy or reliability
Solution Approach 2:
The system dynamically adjusts navigation by allowing the robot to switch between autonomous operation and pre-defined pathways based on real-time obstacle detection. The pathway selection is dynamic and adaptive, combining the benefits of automation with reliability through conditional use of pre-planned routes
2Ease of operation
If mobile robots use traditional pathfinding algorithms, then robots can determine paths autonomously, but manual effort and intervention increase when obstacles require path recalculation
Solution Approach 1:
Multiple alternative pathways are pre-calculated and stored before the robot encounters obstacles. When an obstacle is detected, the robot can immediately switch to a pre-defined alternative pathway without performing time-consuming recalculation, thus reducing manual intervention and saving time
Solution Approach 2:
The system merges traditional autonomous pathfinding with pre-defined pathways by combining real-time obstacle detection with pre-calculated route options. This hybrid approach maintains ease of autonomous operation while eliminating time-consuming recalculation through integrated use of pre-prepared pathways
3Productivity
If mobile robots follow single optimal pathways, then navigation efficiency is improved, but adaptability to unexpected obstacles deteriorates
Solution Approach 1:
The navigation system is segmented into multiple independent pathways instead of relying on a single optimal route. Each pathway is a complete alternative from start to destination, allowing the robot to efficiently switch between segments (pathways) when obstacles are encountered, maintaining both navigation efficiency and adaptability
Solution Approach 2:
The system dynamically selects among multiple pre-defined pathways based on real-time obstacle conditions. The robot maintains adaptability by switching pathways dynamically while preserving navigation efficiency through use of pre-optimized routes rather than recalculating from scratch
4Reliability
If mobile robots allow manual intervention for path determination, then path optimality can be improved through human expertise, but automation level and operational independence deteriorate
Solution Approach 1:
The system enables self-service by allowing the robot to autonomously select and follow optimal pathways from pre-defined options based on real-time obstacle detection. The robot serves itself by making intelligent pathway selections without requiring manual intervention, thus maintaining both path optimality and operational independence
Solution Approach 2:
The system uses feedback from obstacle detection sensors to automatically select the most appropriate pre-defined pathway. This closed-loop feedback mechanism allows the robot to maintain path optimality by responding to environmental conditions while preserving automation through automatic decision-making based on sensor input
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods to implement preferred pathways in mobile robots. Example methods may include obtaining, via at least one of a user interface and a corresponding Application Programming Interface API call, at least one preferred pathway for an autonomous mobile robot, transmitting the at least one preferred pathway to the autonomous mobile robot, generating a planned path for the autonomous mobile robot based at least in part on an influence function, the influence function being representative of an amount of bias towards the at least one preferred pathway on a motion planning decision of the autonomous mobile robot, the amount of bias being based at least in part on a metric associated with the at least one preferred pathway, and causing the autonomous mobile robot to move from a start point to an end point along the planned path.