Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Abstract

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty.

Adaptivity: a path towards general swarm intelligence?

“…This naturally allows for a higher degree of adaptivity due to the increased abilities of an MRS to modulate and vary its behavior, better allowing the system to maintain its performance should the operating conditions change. It is worth noting that a few initial attempts have been made in using these methods, for instance, in systems making use of Lévy walks, individual agents autonomously varying their individual Lévy parameters to modulate the level of exploration and exploitation being carried out ( Pang et al., 2019 ; Nauta et al., 2020 ; Garcia-Saura et al., 2021 ; Kwa et al., 2022a ). Along the same vein, Esterle (2018b) developed a swarm robotic system with agents autonomously switching between two states, thereby allowing the system to track targets that continuously appear and disappear in the environment.…”