Swarm Path Planning for Formation Flight and Obstacle Avoidance
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Solution Overview
Problem
Current navigation systems for unmanned aerial vehicles (UAVs) and other autonomous vehicles lack efficient methods to autonomously navigate through complex environments while maintaining formation and avoiding obstacles, especially in dynamic and uncertain conditions.
Innovation Solution
The Swarm Path Planner (SPP) system determines leader and follower travel directions using environmental and relational measures, employing a cost graph algorithm to plan optimal paths that minimize traversal cost while maintaining desired positional relationships within a swarm, utilizing sensor data and probability functions to adjust for obstacles and dynamic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a navigation system uses simple GPS route navigation, then the system is easy to operate and low cost, but it cannot avoid obstacles or adapt to complex environments
Solution Approach 1:
The navigation system is segmented into multiple independent modules: sensor array for environmental perception, object detection system for identifying obstacles, path planning component for route determination, and control system for execution. Each module performs a specific function, allowing the system to adapt to complex environments through modular integration while keeping individual components manageable in complexity
Solution Approach 2:
The system performs preliminary environmental scanning and obstacle detection before final path determination. The sensor array continuously maps the environment ahead of the vehicle, and potential obstacles are identified and classified in advance, allowing the path planning algorithm to pre-calculate safe routes before the vehicle reaches critical decision points
2Adaptability or versatility
If a navigation system uses onboard imaging and obstacle identification, then it can navigate complex environments, but the system becomes more complex and computationally intensive
Solution Approach 1:
Multiple sensing functions (range finding, imaging, obstacle detection) are merged into a single integrated sensor array system. The object detection system combines data from various sensors to create a unified environmental model, reducing overall system complexity while maintaining comprehensive obstacle avoidance capability through sensor fusion
Solution Approach 2:
An intermediary processing layer is introduced between the sensors and the path planning algorithm. The object detection system acts as a mediator that filters, classifies, and prioritizes detected objects, transforming raw sensor data into structured information that the path planning component can efficiently process, thereby reducing computational intensity
3Adaptability or versatility
If remote control is used with line of sight requirement, then the system is simple to implement, but it cannot operate in environments where line of sight is blocked
Solution Approach 1:
The vehicle is equipped with autonomous navigation capabilities that allow it to independently perceive its environment, detect obstacles, plan paths, and execute maneuvers without continuous human intervention. The onboard sensor array and processing systems enable the vehicle to serve itself in navigation tasks, eliminating the line-of-sight requirement for remote control while operating in complex or blocked environments
Data Source
AI summary
A system for autonomously determining optimal paths without collision through a travel volume for a swarm of vehicles is disclosed. The system determines a travel path for the swarm leader vehicle using a minimal cost path derived from various measures of environmental cost for avoiding objects in traveling from leader location to target location. The system also determines, for each empty neighbor location of each follower vehicle, relational costs for follower vehicle travel relative to leader vehicle travel. The various measures of relational cost seek to maintain a prescribed positional relationship between each follower vehicle and the leader vehicle given the leader vehicle travel path. Based on various measures of environmental and relational cost, the system determines the best travel path for the each follower vehicle relative to the leader vehicle.