Aug 2026· 2026 IEEE 22nd International Conference on Automation Science and Engineering (CASE)· pp. 171-177· 0 citations· 29 references
TL;DR
This paper presents a fast adaptive planning method for tractor-trailer systems that resolves parameter uncertainties by adapting a nominal trajectory to true parameters via iLQR, eliminating the need to store MPs for various configurations.
Abstract
To tackle the computation challenges arising from the stringent positioning accuracy requirement as well as the uncertainties in system dynamics, this paper presents a fast adaptive planning method for tractor-trailer systems. Building on the delayed-expansion A-search-guided tree (DE-AGT) that grows a tree from pre-computed motion primitives (MPs) via A*-like search, Adaptive-iAGT contributes: (i) an iterative Linear-Quadratic Regulator (iLQR) steering module that reaches the goal without a reference trajectory, reducing the planning time; (ii) a two-stage framework that resolves parameter uncertainties by adapting a nominal trajectory to true parameters via iLQR, eliminating the need to store MPs for various configurations; and (iii) analytical control scaling that lets iLQR ignore velocity/control bounds and reinstates them afterwards, increasing the success rate. Simulations and field tests validate the effectiveness of the proposed algorithm.
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