The case for designing optimal strategies for robot locomotion has increased in significance over the past decade with the increasingly large number of unmanned mobile bots being utilized in covert operations. Furthermore, such mobile sensory nodes can be of paramount importance in conducting surveillance and rescue operations for post-disaster recovery teams. The key issue here is to design locomotion and path-planning strategies for bots such that they can operate even in regions with limited or intermittent network connectivity. In this paper, we adapt a variant of the much popular chemotaxic movement algorithm as prevalent amongst bacteria of most strains. Using such a movement strategy the bacteria gradually move towards their location, in search of food, following a chemical gradient. Suboptimal paths are periodically rejected using a process referred to as "tumbling". Using such stochastic techniques, even simplistic creatures like the bacteria reach optimal resources with little inter-communication. This paper analyses and demonstrates such a chemotaxic strategy and explains its analogical relevance in the context of target finding in miniaturized mobile sensory nodes. The paper also throws light on how future resource-aware variants of similar algorithms can be utilized to further optimize path planning strategies for such miniaturized ant-like bots.