Multi-Robot Route Planning for Collision and Congestion Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current route planning systems for autonomous vehicles in confined spaces, such as warehouses, struggle to scale with increasing numbers of robots, leading to cumbersome re-planning and interference from obstacles, and fail to integrate with existing infrastructure, resulting in inefficient navigation and increased collision risks.
Innovation Solution
A cloud-based multi-robot route planner that utilizes node resolution, data structures, and heuristic analysis to optimize route plans, dynamically creating alternate paths and applying cost functions to minimize collisions and congestion, allowing for efficient navigation of multiple autonomous vehicles in complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional route planning systems are used for multiple autonomous vehicles, then basic navigation is achieved, but the system cannot scale with increasing numbers of robots and becomes cumbersome
Solution Approach 1:
The route planning system is segmented into multiple independent modules including a route generation module, a collision detection module, and an optimization module. Each module handles specific aspects of route planning independently, allowing the system to scale by adding more modules or increasing computational capacity without fundamentally redesigning the entire system.
Solution Approach 2:
The system dynamically adjusts route plans in real-time based on changing environmental conditions and robot positions. The route generation module continuously creates new routes, and the optimization module adapts existing routes as robots move and obstacles appear, enabling the system to handle increasing numbers of robots without becoming static or rigid.
2Reliability
If route plans are generated without considering obstacles, then route generation is simple, but collisions and safety hazards occur
Solution Approach 1:
The collision detection module performs preliminary analysis of generated routes to identify potential collisions before robots execute them. By detecting and resolving collision risks in advance, the system ensures safe navigation without requiring complex real-time intervention during robot movement.
Solution Approach 2:
The system continuously monitors robot positions and environmental changes, feeding this information back to the route generation and optimization modules. This feedback loop enables the system to adjust routes dynamically to avoid collisions while maintaining efficient navigation.
3Productivity
If re-planning is performed frequently to handle dynamic environments, then route optimization improves, but computational burden increases
Solution Approach 1:
The optimization module performs partial re-planning only for routes that are affected by changes in the environment or robot positions, rather than re-planning all routes. This selective approach maintains optimization efficiency while minimizing computational overhead and re-planning time.
Data Source
AI summary
A system and a method to optimize route plans for handling critical scenarios faced in an operating environment have been described. The system or a platform resolves one or more nodes based on the inputs related to an operating environment. The system plans one or more routes based on the resolved nodes to provide generated route plans. Based on the planning, the system may analyze one or more route plans for critical scenarios, for example, avoiding a collision or minimizing congestion, damage to the robot, performance of vehicle or warehouse, etc. After the route plans are analyzed, the system optimizes one or more route plans to provide optimized route plans. The optimized route plans are distributed to one or more autonomous vehicles. The fleet of autonomous vehicles may then route progress messages and share feedback with the platform.