BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy(BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior from risk-averse to risk-seeking based on signal reliability and mission urgency. The framework provides theoretical convergence guarantees under unimodal signal assumptions and practical stability under bounded disturbances. Experimental validation across DARPA SubT and multi-room scenarios demonstrates that BEASST consistently outperforms state-of-the-art methods, achieving 15% reduction in path length and 20% faster source localization through intelligent uncertainty-driven navigation that dynamically transitions between aggressive pursuit and cautious exploration.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-Discrete Optimal Transport: Nearly Minimax Estimation With Stochastic Gradient Descent and Adaptive Entropic Regularization
Optimal Transport (OT) based distances are powerful tools for machine learning to compare probability measures and manipulate them using OT maps. In this field, a setting of interest is semi-discrete OT, where the source…
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Adding entropic regularization to Optimal Transport (OT) problems has become a standard approach for designing efficient and scalable solvers. However, regularization introduces a bias from the true solution. To mitigate…
Hessian stability and convergence rates for entropic and Sinkhorn potentials via semiconcavity
In this paper we determine quantitative stability bounds for the Hessian of entropic potentials, i.e., the dual solution to the entropic optimal transport problem. Up to authors' knowledge this is the first work addressi…
Neural Thermodynamics I: Entropic Forces in Deep and Universal Representation Learning
With the rapid discovery of emergent phenomena in deep learning and large language models, explaining and understanding their cause has become an urgent need. Here, we propose a rigorous entropic-force theory for underst…
Deep LearningRepresentation LearningEntropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention
Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear progression in token time. We propose a fundamentally different paradigm: en…