Swarm Path Planning for Obstacle Avoidance and Formation Control
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Solution Overview
Problem
Current navigation systems for unmanned aerial vehicles (UAVs) and other autonomous vehicles face challenges in efficiently planning travel paths to avoid obstacles while maintaining desired formations and positional relationships within swarms, especially in dynamic environments with moving objects and varying terrain.
Innovation Solution
The Swarm Path Planner (SPP) system determines leader and follower travel directions using environmental and relational travel direction techniques, employing probability functions and cost graphs to ensure the shortest path to a target while avoiding obstacles and maintaining swarm formation, integrated with an Object Sense and Avoidance (OSA) system that utilizes sensor arrays for real-time object detection and path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a navigation system uses simple GPS coordinate routing, then the system complexity is low, but the ability to avoid obstacles and adapt to dynamic environments is poor
Solution Approach 1:
The navigation system is segmented into multiple independent modules: GPS receiver module for location acquisition, obstacle detection module for environmental sensing, path planning module for route calculation, and control module for execution. Each module operates independently but communicates through standardized interfaces, allowing the system to adapt to obstacles without requiring complete system redesign.
Solution Approach 2:
The path planning algorithm dynamically adjusts the navigation route based on real-time obstacle detection data. When obstacles are detected, the system recalculates the optimal path using A* or Dijkstra algorithms, enabling continuous adaptation to changing environmental conditions while maintaining systematic operation.
2Reliability
If a navigation system processes images in real-time to identify obstacles, then the obstacle avoidance capability is improved, but the computing time and energy consumption increase
Solution Approach 1:
The system performs preliminary processing of image data by identifying key features and potential obstacles before full path recalculation is required. Edge detection and feature extraction are performed in advance, allowing the main path planning algorithm to work with pre-processed data, reducing overall computing time while maintaining reliable obstacle avoidance.
Solution Approach 2:
When obstacles are detected, the system prioritizes rapid path recalculation by skipping non-critical processing steps and focusing computational resources on determining safe navigation routes. The algorithm rushes through essential calculations to provide timely obstacle avoidance guidance, accepting reduced processing thoroughness in exchange for faster response time.
3Adaptability or versatility
If remote control is used with line of sight requirement, then the system complexity is low, but the operational flexibility and coverage area are limited
Solution Approach 1:
The system introduces an autonomous control intermediary layer between the operator and the vehicle. The onboard computer acts as a mediator that processes sensor data, plans paths, and executes navigation commands independently, while the operator provides high-level guidance. This intermediary enables operation beyond line of sight while maintaining manageable system complexity through modular architecture.
4Reliability
If autonomous control with onboard imaging is used, then the obstacle avoidance capability is improved, but the device complexity and cost increase
Solution Approach 1:
The onboard imaging system serves multiple functions: obstacle detection, terrain mapping, navigation guidance, and mission-specific surveillance. By making the camera system universal and multi-functional, the patent reduces the need for separate specialized sensors, thereby decreasing overall device complexity while maintaining reliable autonomous navigation capability.
Data Source
AI summary
A system for 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.