Parameter estimation for optimal path planning in internal transportation

TL;DR

This paper introduces a novel method for estimating travel costs in mobile robot path planning using a bi-linear state-space model, reducing costs by 15% compared to heuristic approaches without explicit factor modeling.

Contribution

It proposes a new online cost estimation technique based on a bi-linear state-space model, eliminating the need for explicit environmental factor modeling in robot path planning.

Findings

Average path costs are 15% lower with the proposed method.

The method effectively adapts to changing environmental conditions.

It simplifies cost modeling without sacrificing accuracy.

Abstract

The costs incurred in a mobile robot (MR) change due to change in physical and environmental factors. Usually, there are two approaches to consider these costs, either explicitly modelling these different factors to calculate the cost or consider heuristics costs. First approach is lengthy and cumbersome and requires a new model for every new factor. Heuristics cost cannot account for the change in cost due to change in state. This work proposes a new method to compute these costs, without the need of explicitly modelling the factors. The identified cost is modelled in a bi-linear state-space form where the change of costs is formed due to the change of these states. This eliminates the need to model all factors to derive the cost for every robot. In context of transportation, the travel time is identified as the key parameter to understand costs of traversing paths to carry material. The necessity to identify and estimate these travel times is proved by using them in route planning. The paths are computed constantly and average of total path costs of these paths are compared with that of paths obtained by heuristics costs. The results show that average total path costs of paths obtained through on-line estimated travel times are 15\% less that of paths obtained by heuristics costs.