Automatic cable harness layout routing in a customizable 3D environment

TL;DR

The paper tackles automated cable harness topology design in a customizable 3D environment by formulating CHRP as a bi-objective problem that balances total cable length and bundling through a grid-based cost field. It introduces a deterministic Harness Routing Heuristic and two dual-optimization-driven variants (SHRH and α-SPHRH) built on Lagrangian relaxation to generate topologically diverse topology candidates and approximate the Pareto front. Empirical results against stochastic and exact solvers on industrial-sized cases show near-optimal primal performance and favorable compute times, with α-SPHRH offering faster initial routing at the cost of fewer candidates. The work also outlines a design framework and field-cost customization pathway, enabling integration into broader design pipelines and VR-assisted evaluation for practical deployment.

Abstract

Designing cable harnesses can be time-consuming and complex due to many design and manufacturing aspects and rules. Automating the design process can help to fulfil these rules, speed up the process, and optimize the design. To accommodate this, we formulate a harness routing optimization problem to minimize cable lengths, maximize bundling by rewarding shared paths, and optimize the cables' spatial location with respect to case-specific information of the routing environment, e.g., zones to avoid. A deterministic and computationally effective cable harness routing algorithm has been developed to solve the routing problem and is used to generate a set of cable harness topology candidates and approximate the Pareto front. Our approach was tested against a stochastic and an exact solver and our routing algorithm generated objective function values better than the stochastic approach and close to the exact solver. Our algorithm was able to find solutions, some of them being proven to be near-optimal, for three industrial-sized 3D cases within reasonable time (in magnitude of seconds to minutes) and the computation times were comparable to those of the stochastic approach.

Figures (16)

• Figure 1: A cable harness and its terminology.
• Figure 2: Cables and wire harnesses in a Volvo XC90.
• Figure 3: Customized cost fields and optimized routings between pairwise start and end nodes. Green and red grid points correspond to low and high costs, respectively.
• Figure 4: Two optimized harness routings with (\ref{['fig:different_weights_low_bundle']}) low bundle weight and (\ref{['fig:different_weights_high_bundle']}) high bundle weight.
• Figure 5: Execution of the HRH. (\ref{['subfig:local_search_heuristic_a']})$\rightarrow$(\ref{['subfig:local_search_heuristic_b']}): Algorithm \ref{['alg:shortest_path_with_edge_activation']} applied for the red cable. (\ref{['subfig:local_search_heuristic_b']})$\rightarrow$(\ref{['subfig:local_search_heuristic_c']}): Algorithm \ref{['alg:shortest_path_with_edge_activation']} applied for the green cable. (\ref{['subfig:local_search_heuristic_d']}): Optimized branch point locations.