Distributed Mobile Sensor Path Planning for Dynamic Coverage
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Solution Overview
Problem
Existing mobile sensor systems face challenges in efficiently planning paths to achieve optimal image resolution and coverage in diverse environments, particularly in coordinating the motion and orientation of sensors to ensure comprehensive imaging with specified resolution requirements.
Innovation Solution
A distributed path planning method where mobile sensors, such as airborne, ground-based, or underwater systems, optimize their motion and orientation by minimizing a cost function that characterizes image resolution, using a subadditive model for combining overlapping footprints and exchanging local information to achieve a pre-specified desired resolution through a gradient-based optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed objective function with a priori fixed density function is used to maximize joint probability to detect objects, then the detection probability is optimized, but the system cannot adapt to varying environmental conditions and resolution requirements
Solution Approach 1:
The patent transforms the fixed density function into a dynamic resolution function that is updated iteratively based on current sensor positions and orientations. The objective function evolves from a static formulation to a dynamic one that adapts to changing environmental conditions and coverage requirements, allowing the system to maintain optimal performance across varying scenarios.
Solution Approach 2:
The patent implements a feedback mechanism where the density function is updated based on the current state of sensor coverage and positioning. The iterative optimization process uses information from previous iterations to refine the density function, creating a closed-loop system that continuously adapts to achieve desired resolution targets while maintaining detection probability.
2Manufacturing precision
If centralized path planning is used to coordinate sensor motion and orientation, then optimal coverage can be achieved, but the computational complexity and communication requirements increase significantly
Solution Approach 1:
The patent divides the centralized path planning problem into distributed sub-problems, where each sensor independently optimizes its own trajectory and orientation based on local information and the shared density function. This segmentation reduces computational complexity at any single node while maintaining coordinated coverage through the common objective function that all sensors optimize simultaneously.
3Device complexity
If sensors follow pre-planned fixed paths, then the path planning computation is simplified, but the system cannot dynamically adjust to achieve optimal resolution and coverage
Solution Approach 1:
The patent makes the sensor paths dynamic by formulating trajectories as decision variables in an optimization problem rather than fixed predetermined paths. Sensors continuously adjust their positions and orientations to minimize the objective function, enabling real-time adaptation to achieve optimal resolution while keeping the computational framework relatively simple through the use of gradient-based optimization methods.
Data Source
AI summary
A method plans paths of a set of mobile sensors with changeable positions and orientations in an environment. Each sensor includes a processor, an imaging system and a communication system. A desired resolution of coverage of the environment is defined, and an achieved resolution of the coverage is initialized. For each time instant and each sensor, an image of the environment is acquired using the imaging system. The achieved resolution is updated according to the image. The sensor is moved to a next position and orientation based on the achieved resolution and the desired resolution. Then, local information of the sensor is distributed to other sensors using the communication system to optimize a coverage of the environment.