18244912. SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS simplified abstract (Arizona Board of Regents on Behalf of Arizona State University)
Contents
- 1 SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS
Organization Name
Arizona Board of Regents on Behalf of Arizona State University
Inventor(s)
Aniket Shirsat of Tempe AZ (US)
Spring Berman of Scottsdale AZ (US)
Shatadal Mishra of Tempe AZ (US)
Wenlong Zhang of Chandler AZ (US)
SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18244912 titled 'SYSTEMS AND METHODS FOR PROBABILISTIC CONSENSUS ON FEATURE DISTRIBUTION FOR MULTI-ROBOT SYSTEMS WITH MARKOVIAN EXPLORATION DYNAMICS
Simplified Explanation
The abstract describes a consensus-based decentralized multi-robot approach for reconstructing a discrete distribution of features in a planar 2D environment using a random walk model and distributed fusion protocol.
- The approach involves multiple robots exploring the environment through a random walk modeled by a Markov chain.
- Robots estimate the feature distribution using their own measurements and information shared by neighboring robots through a distributed Chernoff fusion protocol.
- The decentralized fusion protocol ensures that each robot's feature distribution converges to the ground truth distribution with high certainty.
Potential Applications
This technology can be applied in various fields such as:
- Surveillance and security systems
- Environmental monitoring
- Search and rescue operations
Problems Solved
This technology addresses the following issues:
- Efficient exploration and mapping of unknown environments
- Collaborative data fusion in decentralized systems
- Accurate estimation of feature distributions in complex environments
Benefits
The benefits of this technology include:
- Improved accuracy and reliability in feature distribution estimation
- Enhanced scalability and adaptability in multi-robot systems
- Reduced communication overhead and centralized processing requirements
Potential Commercial Applications
Potential commercial applications of this technology include:
- Autonomous drones for aerial mapping and monitoring
- Robotic systems for warehouse inventory management
- Collaborative robots for industrial inspection tasks
Possible Prior Art
One possible prior art for this technology could be research on decentralized multi-robot systems for exploration and mapping in unknown environments. Studies on distributed data fusion protocols and collaborative estimation techniques may also be relevant.
Unanswered Questions
How does this technology compare to centralized approaches in terms of efficiency and scalability?
This article does not directly address the comparison between decentralized and centralized approaches in terms of efficiency and scalability. Further research or experimentation may be needed to evaluate the performance differences between the two methods.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not discuss the potential limitations or challenges of implementing this technology in real-world applications. Factors such as hardware constraints, communication reliability, and environmental variability could impact the practicality and effectiveness of the proposed approach. Additional studies or case studies may be necessary to identify and address these challenges.
Original Abstract Submitted
A consensus-based decentralized multi-robot approach is presented for reconstructing a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. Under this decentralized fusion protocol, each robot's feature distribution converges to the ground truth distribution in an almost sure sense.
