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IEEE Transactions on Consumer Electronics

A Novel Hybrid Quantum Architecture for Path Planning in Quantum-Enabled Autonomous Mobile Robots

Mayukh Sarkar, Jitesh Pradhan, Anil Kumar Singh, Hathiram Nenavath

Published Jul 4, 2024• 17 citations
Publisher

Abstract

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.

DOI
10.1109/tce.2024.3423416

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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.
With IoT systems' increasing scale and complexity, maintenance of a large number of nodes using stationary devices is becoming increasingly difficult. Hence, mobile devices are being employed that can traverse through a set of target locations and provide the necessary services. In order to reduce energy consumption and time requirements, the devices are required to traverse following a Hamiltonian path. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, in emergency services, the devices 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, to have a fast solution, the next node selection, which needs to consider all the neighbors for each selection, becomes a bottleneck in the path formation step. Moreover, classical computers are constrained to generate only pseudorandom numbers. Both these problems can be solved using quantum computing techniques, i.e., the next node can be selected with proper randomization, respecting the provided set of probabilities in just a single execution and single measurement of a quantum circuit. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results, thus expecting the proposed work to be important in implementing real-time path planning in quantum-enabled mobile devices.
The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithm overcoming some limitations of the traditional ACO algorithm. However,due to the hardware resource limitations of currently available quantum computers, the practical application of the QACO is still not realized. In this paper, we developed a quantum-classical hybrid algorithm by combining the clustering algorithm with QACO algorithm.This extended QACO can handle large-scale optimization problems with currently available quantum computing resource. We have tested the effectiveness and performance of the extended QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks, and found the algorithm achieves better performance under multiple diverse datasets. In addition, we investigated the noise impact on the extended QACO and evaluated its operation possibility on current available noisy intermediate scale quantum(NISQ) devices. Our work shows that the combination of the clustering algorithm with QACO effectively improved its problem solving scale, which makes its practical application possible in current NISQ era of quantum computing.
Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. An algorithm referred to as Quantum Approximate Optimization Algorithm (QAOA) has been recently proposed to approximately solve hard problems using a protocol know as bang–bang. The technique is based on unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. The QAOA was explored in the context of several optimization problems such as the Max-Cut problem and the Traveling Salesman Problem (TSP). Due to the relatively small node-size solution capability of the available quantum computers and simulators, we developed a hybrid approach where sub-graphs of a TSP tour can be executed on a quantum computer, and the results from the quantum optimization are combined for a further optimization of the whole tour. Since the local optimization of a sub-graph is prone to becoming trapped in a local minimum, we overcame this problem by using a parallel Ant Colony Optimization (ACO) algorithm with periodic pheromone exchange between colonies. Our method exceeds existing approaches which have attempted partitioning a graph for small problems (less than 48 nodes) with sub-optimal results. We obtained optimum results for benchmarks with less than 150 nodes and results usually within 1% of the known optimal solution for benchmarks with around 2000 nodes.
The Traveling Salesman Problem (TSP) is a canonical example of an NP-hard combinatorial optimization problem that has extensive applications in the context of logistics and routing and network design, where classical methods cannot be used in practice due to the large scale of the instances. The approach includes extensive research on a hybrid quantum and classical optimisation model, termed Grover-ACO, that combines the Grover quantum amplitude amplification with the elitist selection stage of the Ant Colony Optimization (ACO). This study can be described as an algorithm that includes candidate pool formation, quantum oracle design, amplitude amplification, and pheromone update algorithms, and give a mathematical model of the hybrid algorithm. The evaluation of standard TSPLIB benchmark instances is conducted experimentally on a quantum circuit simulator, and performance is measured in terms of the best and average tour costs, as well as computational overhead. Findings indicate that GroverACO is consistently able to achieve better quality solutions and discourage premature convergence than classical ACO, especially on medium and large-scale problems, albeit at a higher computational budget due to quantum simulation.
The Traveling Salesman Problem (TSP) is a canonical example of an NP-hard combinatorial optimization problem that has extensive applications in the context of logistics and routing and network design, where classical methods cannot be used in practice due to the large scale of the instances. The approach includes extensive research on a hybrid quantum and classical optimisation model, termed Grover-ACO, that combines the Grover quantum amplitude amplification with the elitist selection stage of the Ant Colony Optimization (ACO). This study can be described as an algorithm that includes candidate pool formation, quantum oracle design, amplitude amplification, and pheromone update algorithms, and give a mathematical model of the hybrid algorithm. The evaluation of standard TSPLIB benchmark instances is conducted experimentally on a quantum circuit simulator, and performance is measured in terms of the best and average tour costs, as well as computational overhead. Findings indicate that GroverACO is consistently able to achieve better quality solutions and discourage premature convergence than classical ACO, especially on medium and large-scale problems, albeit at a higher computational budget due to quantum simulation.