Autonomous Searching for a Diffusive Source Based on Minimizing the Combination of Entropy and Potential Energy

Abstract

The infotaxis scheme is a search strategy for a diffusive source, where the sensor platform is driven to reduce the uncertainty about the source through climbing the information gradient. The infotaxis scheme has been successfully applied in many source searching tasks and has demonstrated fast and stable searching capabilities. However, the infotaxis scheme focuses on gathering information to reduce the uncertainty down to zero, rather than chasing the most probable estimated source when a reliable estimation is obtained. This leads the sensor to spend more time exploring the space and yields a longer search path. In this paper, from the context of exploration-exploitation balance, a novel search scheme based on minimizing free energy that combines the entropy and the potential energy is proposed. The term entropy is implemented as the exploration to gather more information. The term potential energy, leveraging the distance to the estimated sources, is implemented as the exploitation to reinforce the chasing behavior with the receding of the uncertainty. It results in a faster effective search strategy by which the sensor determines its actions by minimizing the free energy rather than only the entropy in traditional infotaxis. Simulations of the source search task based on the computational plume verify the efficiency of the proposed strategy, achieving a shorter mean search time.

Autonomous Search of Radioactive Sources through Mobile Robots

“…If the estimation is not accurate, the robot cannot find the source. The dynamically updated search method [27][28][29][30][31][32] can make up for this shortcoming well. For example, in [27] the dynamic programming method for robot chemical source tracking was proposed.…”

“…In addition, the probability of detection and recognition was correlated with the robot's current position and target distance though using the Markov decision process, to minimize the search time. In [29][30][31][32], POMDP was used to design the gas source search algorithm and the Bayesian frame was used to estimate the gas source parameters. PDF acted as the information state in POMDP, and the gas source search was completed through the reward mechanism and behavior selection.…”

“…In [31], relative entropy (also known as Kullback-Leibler divergence) was used as a reward for POMDP. The authors in [32] combined the potential energy and the entropy into free energy to be minimized as the reward of POMDP. The studies in [29][30][31][32] provided good ideas for the autonomous search algorithm for radioactive sources in this paper.…”

“…(2) In this paper, the speed and angular velocity of the robot are related together as a limited set of actions for POMDP. However, in reference [ 29 , 30 , 31 , 32 ] the POMDP behavior set was a single direction indicating behavior, such as . The limited action set method provided in this paper can make the robot more maneuverable in the search process.…”

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

“…Ruddick et al [ 33 ] extended the Infotaxis algorithm to three-dimensional space for application and applied the algorithm to the actual environment for verification. In 2019, Song et al [ 34 ] proposed the minimum free energy search method based on the combination of entropy and potential energy in Monte Carlo. Hai-Feng et al [ 35 ] proposed an underwater chemical plume tracking system, which used the combination of partially observable Markov decision algorithm and artificial potential field algorithm to construct the underwater source probability map.…”