Pathfinding on grid maps with obstacles is a fundamental problem in robotics, autonomous mechatronic systems, and artificial intelligence. Classical algorithms such as BFS, Dijkstra, and A* guarantee optimal solutions under specific conditions, but often explore many redundant nodes, leading to increased computational overhead and memory saturation. This paper introduces an advanced, probabilistically guided path planning method based on the Grover probability distribution. The quantum-inspired heuristic leverages Grover’s algorithm framework to generate a highly targeted probabilistic guidance map, biasing the search toward more promising spatial corridors. To ensure methodological rigor, benchmarks are scaled up to a 30 × 30 grid and comprehensively evaluated across 30 randomly generated map instances with strict statistical analysis. Furthermore, the 8-directional movement framework is corrected to incorporate an exact geometric cost of 2 for diagonal transitions, preventing valuation errors common in simplified benchmarks. Experiments demonstrate that the Grover-guided approach significantly reduces the number of visited nodes compared to classical BFS and Dijkstra while maintaining strict path quality and completeness. Finally, I report and discuss the actual execution times, addressing the classical computational overhead introduced by simulating quantum iterations on conventional hardware, and charting a path toward real-time deployment on genuine quantum processing units (QPUs) for future mechatronic applications.
In contemporary virtual vehicle development based on systems engineering and the V-Model methodology, two primary approaches are used for simulating longitudinal dynamics: quasi-static backward simulation models and dynamic forward simulation models. Each approach offers distinct advantages and disadvantages. The main benefit of quasi-static backward simulation models is their computational efficiency. However, this type of model does not capture vehicle dynamics, which is a strength of forward simulation models. Additionally, forward simulation models inherently require longer computation times, which significantly increases the overall simulation effort, particularly when combined with optimization across multiple iterations. Nevertheless, in the context of requirement-based simulation, backward simulation models are generally more suitable than forward simulation models. Therefore, an obvious solution is to include dynamics in a backward simulation model.This publication presents a new time-efficient backward simulation approach that incorporates vehicle dynamics, with a particular focus on the acceleration of battery-electric vehicles (BEVs). The model is constructed using basic component representations, which can be replaced by more detailed models in future work, in line with systems engineering principles. For validation, the outputs of the proposed model are first compared against a quasi-static backward simulation model, a forward simulation reference model and vehicle measurement data. Secondly, a parameter analysis is conducted using a forward simulation model to analyze the chosen parameters of the backward simulation model.
This paper proposes a projection-based layer for constrained path planning, where task requirements are encoded as equality constraints defining an implicit manifold. The method is compatible with geometric planners that optimise a path inside a corridor represented as a sequence of convex safety regions, and returns a collision-free B-spline path, with user-prescribed degree and continuity, that is uniformly ɛ-adherent to the implicit task constraints. Corridor construction is biased toward task feasibility by projecting exploration seeds onto the manifold, so that each corridor element contains at least one feasible configuration. After corridor optimisation, the task constraints are enforced at the B-spline control points within a prescribed projection tolerance through a constrained closest-point problem that preserves corridor containment. Continuity is then recovered by a smoothing procedure based on alternating projections while maintaining the prescribed control-point tolerance. Uniform ɛ-adherence along the whole path is finally enforced by an outer loop that recomputes the knot vector with progressively more knots and repeats optimisation and projection until the prescribed tolerance is satisfied. The approach is validated through simulations and real-world experiments.
Mono-actuated mechatronic systems are widely used in industrial motion applications. In such systems, accurate knowledge of position-dependent system properties is essential for reliable torque prediction and performance optimization. Conventional identification approaches typically rely on analytical modeling or CAD-based simulations, which may become inaccurate due to process modifications, machine wear, or incomplete model information. This paper proposes an industrially applicable run-to-run identification procedure for estimating position-dependent inertia, load torque, and friction directly from motion data acquired on the physical machine. The method operates without interrupting the nominal task of the machine, defined as a point-to-point motion between two positions within a specified time, and employs different motion profiles to ensure sufficient excitation. Model parameters are estimated from repeated task executions, and are iteratively refined over multiple identification runs. The position dependence is represented using polynomials expressed in the Chebyshev basis, with coefficients obtained through a computationally efficient and numerically robust least-squares formulation. The proposed approach enables users to periodically update dynamic models throughout the machine lifecycle. The method is validated on two distinct industrial mechanisms, representing inertia-dominant and load-torque-dominant dynamics. Experimental results demonstrate accurate torque estimation, achieving up to 95% goodness-of-fit. To illustrate the practical utility of the identified model, it is applied in downstream tasks such as torque feedforward control, yielding tracking error reductions of up to 94.8%, and motion profile optimization, resulting in torque reductions of up to 67%.
High-speed on/off valves (HSVs) are pivotal components of digital hydraulic systems (DHSs), primarily governing system response and control precision. However, the high-frequency switching of HSVs results in severe spool-body impacts, which critically compromise the longevity and reliability of DHSs. To address this issue, this study proposes a pre-excitation soft-switching control (PESSC) algorithm that synergistically integrates preloading acceleration and reverse deceleration to enhance dynamics while regulating impact velocity. This PESSC can be used to produce low-velocity impacts, enabling further investigation into the evolution of the wear behavior on the impact surface. The results demonstrate that under a 24 V supply, the PESSC effectively reduces the spool impact velocity from 0.85 to 0.34 m/s while improving the dynamic performance by 25.7%. This optimal balance between fast switching and gentle impact lowers the maximum contact stress of the impact surface from 35.4 to 14.6 MPa, thereby substantially alleviating wear. After 10 million switching cycles, the average surface height reduction decreases from 2.535 to 0.913 μm, and the mass loss decreases from 20.72 to 9.25 mg. Notably, at the reduced velocity, the severe fatigue spalling observed under high-impact conditions (0.85 m/s) is entirely eliminated. These findings substantiate that the proposed PESSC offers a promising and effective strategy for enhancing the durability and reliability of DHSs.
