Industrial robots play a pivotal role in modern manufacturing, yet achieving real-time path planning and reliable collision avoidance in dynamic environments remains a major challenge. Traditional algorithms often fail to balance efficiency, adaptability, and safety. This paper presents a quantum annealing (QA)-based approach to optimize robot navigation by minimizing travel time, energy consumption, and collision risk under operational constraints. By integrating QA with real-time sensor data and robot kinematics, the system dynamically replans trajectories in response to environmental changes. Comparative simulations demonstrate that the QA method outperforms classical algorithms such as Particle Swarm Optimization (PSO) and A-Star (A*) in both computational speed and path quality. These results highlight the potential of quantum annealing as a powerful tool for intelligent robotic navigation in complex industrial settings.
Industrial robots play a pivotal role in modern manufacturing, yet achieving real-time path planning and reliable collision avoidance in dynamic environments remains a major challenge. Traditional algorithms often fail to balance efficiency, adaptability, and safety. This paper presents a quantum annealing (QA)-based approach to optimize robot navigation by minimizing travel time, energy consumption, and collision risk under operational constraints. By integrating QA with real-time sensor data and robot kinematics, the system dynamically replans trajectories in response to environmental changes. Comparative simulations demonstrate that the QA method outperforms classical algorithms such as Particle Swarm Optimization (PSO) and A-Star (A*) in both computational speed and path quality. These results highlight the potential of quantum annealing as a powerful tool for intelligent robotic navigation in complex industrial settings.
ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.
Industrial engineering increasingly depends on solving large-scale optimization problems involving scheduling, routing, resource allocation, inventory control, energy management, and supply-chain coordination. Classical exact methods often become computationally expensive when problem size, uncertainty, and combinatorial complexity grow rapidly. Although practical quantum computers are still developing, quantum-inspired computing approaches have emerged as powerful alternatives that adapt principles from quantum mechanics to run on classical hardware. This paper investigates quantum-inspired computing approaches for complex optimization problems in industrial engineering. It examines methodologies including simulated annealing, tensor-network optimization, Ising formulations, quantum- inspired evolutionary algorithms, digital annealers, and hybrid classical-intelligent heuristics. Applications in production scheduling, facility layout, vehicle routing, maintenance planning, portfolio optimization, warehouse operations, and smart manufacturing are analyzed. Particular attention is given to solution quality, scalability, stochastic decision-making, real-time adaptability, and integration with Industry 4.0 data systems. Benefits include faster near-optimal solutions, improved operational efficiency, reduced energy consumption, and better responsiveness under uncertainty. Major challenges include formulation difficulty, parameter tuning, benchmarking inconsistency, interpretability, and transition barriers within traditional industrial workflows. A future roadmap is proposed involving hybrid quantum-classical ecosystems, AI- guided optimization engines, digital twins, and autonomous decision platforms. The paper concludes that quantum-inspired computing can deliver practical industrial value today by bridging the gap between conventional optimization and future quantum advantage, especially in environments where speed, flexibility, and complexity management are essential.
This paper explores the application of quantum computing to enhance constraint propagation within robotics. Conventional constraint propagation algorithms struggle with complex environments, often requiring extensive simulations. We propose a novel quantum-enhanced technique that leverages quantum algorithms to efficiently explore potential trajectories and constraints, resulting in improved motion planning and robustness. The core mechanism involves utilizing quantum annealing to accelerate the search for optimal solutions, leading to more efficient and robust motion planning. The research focuses on the potential of quantum computing to overcome the limitations of existing constraint propagation methods, specifically addressing challenges in dynamic environments and high-dimensional constraints. We present a preliminary analysis demonstrating improvements in planning efficiency and robustness compared to classical methods.
The earlystage of factory layout planning has a direct effect on the operating costs, efficiency, and productivity of manufacturing systems. Conventional automated planning approaches often face a trade-off between solution quality and computational effort. Recent studies have investigated the potential of quantum annealing (QA) for solving factory layout planning problems. However, existing studies have mainly focused on individual QA-based formulations, whereas current hybrid QA solvers support multiple model types that combine QA and classical methods. To explore the potential of QA for factory layout planning, this paper benchmarks four QA based approaches implemented with the corresponding solvers, including an iterative binary quadratic model (IBQM), a hybrid discrete quadratic model (HDQM), a hybrid constrained quadratic model (HCQM), and a hybrid nonlinear model (HNLM). To improve scalability, an iterative decomposition strategy is developed for IBQM, while a candidate selection procedure is introduced for HDQM. The approaches are benchmarked against genetic algorithm, particle swarm optimization, and a hybrid tabu search and simulated annealing approach on 14 layout instances with different layout sizes, geometric complexity, spatial restrictions, and transport route configurations. The comparative analysis considers solution quality, computation time, and scalability. The results show that QA-based approaches achieve competitive solution quality and substantial computational advantages, reducing computation time by up to 94.51% relative to the classical reference methods. Among them, IBQM obtains the best solution quality in 78.6% of the instances, with a maximum improvement of 17.41% associated with an 81.34% reduction in computation time. These findings provide a basis for their further application in industrial layout planning.
This paper delves into the fusion of quantum computing and robotics, focusing on motion planning in cluttered environments. Traditional algorithms struggle with complex problems where many constraints need to be satisfied. Hence, optimization-based approaches such as Constrained Quadratic Models (CQM) have become increasingly popular. Our work presents a 3D tracking algorithm based on CQM uniquely adapted for quantum computers to address computational challenges. With their parallel processing capabilities, Quantum computers offer a groundbreaking approach to optimizing complex problems. We formulate the CQM problem for efficient resolution on the D-Wave quantum computer, showcasing its superiority over classical counterparts. Our application centers on real-time planning in a target-chaser tracking scenario, highlighting the quantum advantage in handling the computation complexity of constrained problems. This paper bridges the quantum-robotics gap and sets the stage for future research in quantum-enhanced robotic motion planning.
Addressing the challenges of multi-device collaboration, significant dynamic disturbances, and poor optimization performance of traditional algorithms in disassembly scheduling within intelligent manufacturing environments, this paper constructs a multi-objective optimization model integrating total project duration, equipment energy consumption, and load balancing. A weighted approach is employed to handle the coupling of objectives. Constraints are set based on the actual scenario, and the quantum annealing algorithm is simplified and adapted. A dynamic adaptive quantum annealing algorithm (DAQA) is designed based on the traditional quantum annealing algorithm, incorporating three mechanisms: dynamic neighborhood generation, adaptive cooling schedule, and dynamic disturbance compensation. This addresses the bottlenecks of slow convergence, susceptibility to local optima, and weak antidisturbance capability of traditional algorithms. Comparative experiments are conducted using three datasets of different sizes, comparing DAQA with traditional quantum annealing, genetic algorithms, and particle swarm optimization. The results show that DAQA outperforms the compared algorithms in terms of solution accuracy, convergence speed, runtime, robustness, and energy consumption optimization. It can effectively adapt to the dynamic disassembly scenario in intelligent manufacturing, improving scheduling efficiency and resource utilization. Note that the algorithm is currently validated on a classical hardware-based D-Wave simulation platform, and its performance on actual quantum annealers with inherent noise and limited connectivity will be verified in subsequent hardware experiments.