Related Experiment Video
Updated: Apr 3, 2026
06:44
Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
Published on: September 23, 2025
689
Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots
Fernando Martín1, Luis Moreno2, Santiago Garrido3
1Robotics Lab, Carlos III University, Madrid 28911, Spain. fmmonar@ing.uc3m.es.
Sensors (Basel, Switzerland)
|September 22, 2015
Summary
This study enhances mobile robot localization using a novel particle filter combining Markov chain Monte Carlo sampling and Differential Evolution. The approach excels in challenging environments with occlusions from dynamic obstacles.
Area of Science:
- Robotics
- Artificial Intelligence
- Sensor Fusion
Background:
- Mobile robot localization is crucial for autonomous navigation, especially in complex environments.
- Existing methods often struggle with dynamic obstacles and sensor occlusions.
- Previous work explored evolutionary strategies for robot localization.
Purpose of the Study:
- To develop an advanced global localization algorithm for mobile robots.
- To improve localization accuracy in environments with occlusions and dynamic obstacles.
- To present the latest advancements in evolutionary-based localization strategies.
Main Methods:
- A particle filter integrating Markov chain Monte Carlo (MCMC) sampling and Differential Evolution (DE).
- A cost function based on Kullback-Leibler (KL) divergence for asymmetric processing of sensor data.
- Optimization of robot pose estimation using a weighted set of possible locations.
Main Results:
- The algorithm demonstrated excellent performance in a real-world map.
- Effective handling of occlusions caused by dynamic and unmodeled obstacles.
- Robust localization even with asymmetric processing of sensed information.
Conclusions:
- The proposed MCMC-DE particle filter offers a robust solution for mobile robot global localization.
- The KL divergence-based cost function effectively addresses challenges posed by occlusions.
- This method significantly improves robot navigation capabilities in dynamic and unpredictable environments.
Keywords:
Kullback-Leibler divergenceMarkov chain Monte Carlodifferential evolutionglobal localizationlaser range findersmobile robotMore Related Videos
Related Concept Videos
01:25Modeling with Differential Equations
255
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
255
03:33Genetic Drift
45.2K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
45.2K
01:12Differential Leveling
895
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
895
01:09Mutation, Gene Flow, and Genetic Drift
66.0K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
66.0K