The article considers a method for constructing a trajectory of a collaborative mobile robot using quantum computing in combination with classical optimization algorithms.The purpose of the study is to develop a mathematical model and numerical simulation of a quantum-oriented approach to constructing a trajectory of a collaborative robot, which allows minimizing energy costs in motion planning and ensuring obstacle avoidance in a dynamic environment.The scientific novelty of the work lies in the application of quantum optimization methods for multivariate planning, which allows avoiding local minima and guaranteeing the search for globally optimal solutions even in the case of complex configuration spaces.During the numerical simulation, it was demonstrated that the formed trajectories ensure successful obstacle avoidance and reaching the target point without deviations from the grid.Conclusions: analysis of the results showed stabilization of the energy function at the level of -350…-420, which confirms the effectiveness of optimization and convergence to the best solutions after 100-150 iterations.The constructed graphs confirmed that the robot's movement is consistent with the calculated quantum plans, and the deviation between the predicted and executed trajectories is minimized.The use of the energy efficiency criterion allowed us to evaluate different route construction scenarios, where the best plans stabilized with a margin of optimality relative to the worst options by 15-20%.Qualitative analysis of the constructed trajectories confirmed the consistency between the predicted and executed paths, and quantitative results proved a reduction in the number of steps when reaching the target point.The results obtained prove that the proposed method is promising for constructing reliable and energy-optimal trajectories in collaborative robotic systems Industry 5.0.
The article considers a method for constructing a trajectory of a collaborative mobile robot using quantum computing in combination with classical optimization algorithms.The purpose of the study is to develop a mathematical model and numerical simulation of a quantum-oriented approach to constructing a trajectory of a collaborative robot, which allows minimizing energy costs in motion planning and ensuring obstacle avoidance in a dynamic environment.The scientific novelty of the work lies in the application of quantum optimization methods for multivariate planning, which allows avoiding local minima and guaranteeing the search for globally optimal solutions even in the case of complex configuration spaces.During the numerical simulation, it was demonstrated that the formed trajectories ensure successful obstacle avoidance and reaching the target point without deviations from the grid.Conclusions: analysis of the results showed stabilization of the energy function at the level of -350…-420, which confirms the effectiveness of optimization and convergence to the best solutions after 100-150 iterations.The constructed graphs confirmed that the robot's movement is consistent with the calculated quantum plans, and the deviation between the predicted and executed trajectories is minimized.The use of the energy efficiency criterion allowed us to evaluate different route construction scenarios, where the best plans stabilized with a margin of optimality relative to the worst options by 15-20%.Qualitative analysis of the constructed trajectories confirmed the consistency between the predicted and executed paths, and quantitative results proved a reduction in the number of steps when reaching the target point.The results obtained prove that the proposed method is promising for constructing reliable and energy-optimal trajectories in collaborative robotic systems Industry 5.0.
Autonomous mobile robots are being used increasingly as consumer devices around the globe, such as for fetching items and cleaning purposes, to name a few, in households and industries. Such robots are being employed to traverse a set of target locations and provide the necessary services. In a 2-dimensional environment, these robots are required to traverse following a Hamiltonian path to reduce energy consumption and time requirements. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, upon urgent requirements, these robots must traverse in real-time, demanding speedy path planning from the TSP instance. Among the well-known optimization techniques for solving the TSP problem, Ant Colony Optimization has a good stronghold in providing good approximate solutions. Moreover, ACO not only provides near-optimal solutions for TSP instances but can also output optimal or near-optimal solutions for many other demanding hard optimization problems. However, most of the implementations of Ant Colony Optimization on quantum or hybrid quantum architecture proposed in the literature require conversion of classical data to qubits before being fed to the algorithm, and cannot be automated. But quantum-enabled mobile robots require automated path formation after receiving the commands from the environment. The novelties of the proposed work are many-fold. Firstly, the proposed work allows ACO to be applied as its classical counterparts, allowing automation in path formation in quantum-enabled mobile robots. Secondly, this allows a new way of incorporating quantum processing unit in the research of quantum-enabled mobile robots. Researchers around the globe have been trying to incorporate quantum computing in autonomous mobile robots, and true to the best of authors’ knowledge, no work in path planning for multiple targets in quantum-enabled mobile robots have been found in literature. Thirdly, quantum processing unit has been applied at exactly that point where it will be most useful, as in NISQ era quantum computer is not reliable for arithmetic processing. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results with average error percentage from optimumresults of only 6.985%. Hence, it is expected that the proposed work to be important in future research of fusing the two rising domains of quantum computing and autonomous mobile robots.
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.
Quantum computing has gained attention for its potential to surpass classical computing in large-scale computations. In this study, we propose a method for solving the inverse kinematics of a robot using quantum computing. The approach leverages the ability of qubits to represent points on a sphere in three-dimensional space. Forward kinematics calculations are performed using qubits that encode the posture of each robot link, while inverse kinematics solutions are obtained through iterative optimization on a classical computer. Furthermore, we demonstrate that the robot's end-effector position can be effectively represented using a 2-qubit rotation gate, where the root joint angle influences the tip joint angle, resulting in accelerated convergence during inverse kinematics optimization. The proposed method was validated on an actual quantum computer, confirming its feasibility and efficiency. These findings suggest that hybrid quantum-classical approaches can enhance robotic motion planning and optimization in future quantum computing applications.
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.
Computational resources of quantum computing can enhance robotic motion, decision making, and path planning. While the quantum paradigm is being applied to individual robots, its approach to swarms of simple and interacting robots remains largely unexplored. In this paper, we attempt to bridge the gap between swarm robotics and quantum computing, in the framework of a search and rescue mission. We focus on a decision-making and path-planning collective task. Thus, we present a quantum-based path-planning algorithm for a swarm of robots. Quantization enters position and reward information (measured as a robot’s proximity to the target) and path-planning decisions. Pairwise information-exchange is modeled through a logic gate, implemented with a quantum circuit. Path planning draws upon Grover’s search algorithm, implemented with another quantum circuit. Our case study involves a search and rescue scenario, inspired by ant-foraging behavior in nature, as an example of swarm intelligence. We show that our method outperforms two ant-behavior simulations, in NetLogo and Java, respectively, presenting a faster convergence to the target, represented here by the source of food. This study can shed light on future applications of quantum computing to swarm robotics.
Path planning in complex and obstacle-constrained environments remains a challenging combinatorial optimization problem due to the exponential growth of the solution space and the limitations of classical algorithms in dynamic decision-making scenarios. Traditional approaches such as Dijkstra or A* may experience high computational costs and susceptibility to local optima when dealing with large-scale directed graphs containing obstacles. This study proposes a hybrid quantum computing approach that integrates the Grover Search Algorithm and the Quantum Approximate Optimization Algorithm (QAOA) to address optimal path planning on obstacle-oriented directed graphs. The Grover algorithm is first employed as an oracle-based filtering mechanism to detect and eliminate obstacle-containing edges, thereby generating a valid subgraph of feasible paths. Following this preprocessing step, QAOA is applied to the filtered graph within a nonlinear optimization framework to determine the minimum-cost path between source and destination nodes. The optimization model represents the graph as G=(V,E), where binary decision variables indicate whether an edge is included in the selected route while satisfying flow conservation constraints. By narrowing the solution space through Grover-based obstacle detection and performing optimization only on valid edges, the proposed method significantly reduces computational complexity compared to conventional approaches. Experimental analysis demonstrates that the hybrid quantum model effectively identifies optimal routes and adapts to different obstacle configurations while maintaining efficient convergence behavior. The results highlight the potential of integrating quantum search and quantum optimization techniques for solving combinatorial graph problems. This work contributes to the growing body of research on quantum-assisted optimization and demonstrates the feasibility of applying hybrid quantum algorithms to directed path planning problems relevant to autonomous navigation systems.