Generalizing Multi-Objective Search via Objective-Aggregation Functions
This work addresses the challenge of adapting standard multi-objective search algorithms for complex, real-world robotics problems, offering a practical solution for domains like navigation and medical systems.
The paper tackles the problem of applying multi-objective search algorithms to complex robotics tasks with conflicting objectives by introducing a generalized formulation using objective-aggregation functions, and demonstrates that this approach outperforms vanilla algorithms by orders of magnitude in diverse planning scenarios.
Multi-objective search (MOS) has become essential in robotics, as real-world robotic systems need to simultaneously balance multiple, often conflicting objectives. Recent works explore complex interactions between objectives, leading to problem formulations that do not allow the usage of out-of-the-box state-of-the-art MOS algorithms. In this paper, we suggest a generalized problem formulation that optimizes solution objectives via aggregation functions of hidden (search) objectives. We show that our formulation supports the application of standard MOS algorithms, necessitating only to properly extend several core operations to reflect the specific aggregation functions employed. We demonstrate our approach in several diverse robotics planning problems, spanning motion-planning for navigation, manipulation and planning fr medical systems under obstacle uncertainty as well as inspection planning, and route planning with different road types. We solve the problems using state-of-the-art MOS algorithms after properly extending their core operations, and provide empirical evidence that they outperform by orders of magnitude the vanilla versions of the algorithms applied to the same problems but without objective aggregation.