Coverage Path Planning for Decomposition Reconfigurable Grid-Maps Using Deep Reinforcement Learning Based Travelling Salesman Problem

Kyaw, Phone Thiha; Paing, Aung; Thu, Theint Theint; Elara, Mohan Rajesh; Le, Anh Vu; Veerajagadheswar, Prabakaran

doi:10.1109/access.2020.3045027

Cited by 56 publications

(38 citation statements)

References 33 publications

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“…Specifically, we employed the actor-critic methods [34] to learn approximations to both the policy and value functions of the RL problem. Two neural networks were utilized to represent the actor and critic networks, similarly to the work of [30]. Both networks employed the pointer network architecture [35], consisting of a pair of RNNs (encoders and decoders), each containing long short-term memory (LSTM) layers [36] to parameterize the trained policy and value model.…”

Section: Optimization With Reinforcement Learningmentioning

confidence: 99%

“…Both networks employed the pointer network architecture [35], consisting of a pair of RNNs (encoders and decoders), each containing long short-term memory (LSTM) layers [36] to parameterize the trained policy and value model. For further details on the neural network architecture, we refer to the works of [30,35].…”

Section: Optimization With Reinforcement Learningmentioning

confidence: 99%

“…The recent paper utilizes network-based deterioration introduced by the author in [26]. Numerous techniques allow us to partition the workspace utilizing various learning-based methodologies, for example, tree scaling, energy acknowledgement calculations [27,28], AI-based deep learning [29] and grid-cellbased guide deterioration [30]. These approaches are lightweight in computational intricacy to make a decomposition map for effective CPP.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Veerajagadheswar

Kyaw

et al. 2021

Sensors

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One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot’s goal enables the complete coverage of the entire area by reconfiguring to different shapes according to the area’s needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving energy usage during navigation. This implies that the robot is able to cover larger areas entirely with the least required actions. This paper presents a complete path planning (CPP) for hTetran, a polyabolo tiled robot, based on a TSP-based reinforcement learning optimization. This structure simultaneously produces robot shapes and sequential trajectories whilst maximizing the reward of the trained reinforcement learning (RL) model within the predefined polyabolo-based tileset. To this end, a reinforcement learning-based travel sales problem (TSP) with proximal policy optimization (PPO) algorithm was trained using the complementary learning computation of the TSP sequencing. The reconstructive results of the proposed RL-TSP-based CPP for hTetran were compared in terms of energy and time spent with the conventional tiled hypothetical models that incorporate TSP solved through an evolutionary based ant colony optimization (ACO) approach. The CPP demonstrates an ability to generate an ideal Pareto optima trajectory that enhances the robot’s navigation inside the real environment with the least energy and time spent in the company of conventional techniques.

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Section: Optimization With Reinforcement Learningmentioning

confidence: 99%

Section: Optimization With Reinforcement Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Veerajagadheswar

Kyaw

et al. 2021

Sensors

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“…The traveling salesman problem (TSP) is one of the bestknown combinatorial optimization problems and is often considered in autonomous vehicle route planning [11,19,31,48,50,65,80]. In a TSP, the sequence of autonomous agent movements should optimize a route between a set of nodes [3,16,32,33,55].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the traveling salesman problem with refueling

Ottoni

Nepomuceno

Oliveira

et al. 2021

Complex Intell. Syst.

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The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. Many methods derived from TSP have been applied to study autonomous vehicle route planning with fuel constraints. Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. This paper employs RL to solve the traveling salesman problem With refueling (TSPWR). The technique proposes a model (actions, states, reinforcements) and RL-TSPWR algorithm. Focus is given on the analysis of RL parameters and on the refueling influence in route learning optimization of fuel cost. Two RL algorithms: Q-learning and SARSA are compared. In addition, RL parameter estimation is performed by Response Surface Methodology, Analysis of Variance and Tukey Test. The proposed method achieves the best solution in 15 out of 16 case studies.

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“…To deal with the CCPP problem in a large complex environment, Kyaw et al. formulated it using TSP and deep reinforcement learning (DRL) [ 20 , 21 ]. The recurrent neural network (RNN) was trained using RL and combined with the cellular decomposition method to iteratively generate the coverage path.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

Lin

Huang

2021

Sensors

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In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.

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Coverage Path Planning for Decomposition Reconfigurable Grid-Maps Using Deep Reinforcement Learning Based Travelling Salesman Problem

Cited by 56 publications

References 33 publications

Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Reinforcement learning for the traveling salesman problem with refueling

Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

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