Inference Based Multi-Object Reactive Search in a Partially Known Environment With Temporal Logic Specifications

Abstract

Efficiently searching for multiple objects in a partially known environment, where only the names and locations of landmarks are available, presents significant challenges. Existing search algorithms in the literature fail to fully utilize prior knowledge to improve search efficiency, and exhibit significantly diminished efficiency when extended to multiobject search. To address these limitations, we propose an inference-based multi-object reactive search framework. This framework utilizes the COMET inference model to reason about co-occurrence values between the target objects and known landmarks, thereby enhancing search efficiency. These co-occurrence values are integrated into a reactive temporal logic motion planning strategy, which allows the robot search for multiple objects with temporal logic constraints specified by LTL and adapt dynamically if the inferred reasoning differs from the actual object arrangement encountered during the search. Extensive simulations were conducted to evaluate the feasibility and efficiency of the proposed motion planning algorithm. Results demonstrate that the integration of commonsense reasoning with reactive temporal logic planning significantly improves multi-object search efficiency. Project website: https://sites.google.com/view/imors.