Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Niloy, Md. A. K.; Shama, Anika; Chakrabortty, Ripon K.; Ryan, Michael J.; Badal, Faisal R.; Tasneem, Zinat; Ahamed, Md. Hafiz; Moyeen, Sumaya I.; Das, Sajal K.; Ali, Feroz; Islam, Rafiqul; Saha, Dip Kumar

doi:10.1109/access.2021.3062557

Cited by 80 publications

(50 citation statements)

References 276 publications

(232 reference statements)

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“…Inductive learning (IL) in GNN can discover unseen data more effectively than transductive learning. IL can handle graph-based models dynamically that creates this strategy to solve real-world problems [175]. This setting also enables the use of transfer learning (TL) [176].…”

Section: D: Transfer Learningmentioning

confidence: 99%

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

et al. 2021

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This review provides a comprehensive overview of the state-of-the-art methods of graphbased networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fixed neural network trying to find the pattern in feature space. These strategies have successfully been applied to many applications for euclidean space data. Since graph data in a non-euclidean space does not follow any kind of order, these solutions can be applied to exploit the node relationships. Graph Neural Networks (GNNs) solve this problem by exploiting the relationships among graph data. Recent developments in computational hardware and optimization allow graph networks possible to learn the complex graph relationships. Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints. To encapsulate the importance of graph models, in this paper, we formulate a systematic categorization of GNN models according to their applications from theory to real-life problems and provide a direction of the future scope for the applications of graph models as well as highlight the limitations of existing graph networks.INDEX TERMS Graph neural network, geometric deep learning, graph-structured network, non-euclidean space.

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Section: D: Transfer Learningmentioning

confidence: 99%

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

et al. 2021

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“…Robot poses along the trajectory and the observable landmarks in the environment under investigation are represented as vertices in the graph. In case landmarks are not inserted into the map and only robot poses are considered, the algorithm is referred to as pose-graph optimization [2], [3]. Spatial constraints, that are formulated based upon the sensory measurements collected by the robot, are encoded as edges that connect the graph vertices.…”

Section: Introductionmentioning

confidence: 99%

“…A solution to a graph SLAM problem is referred to as globally consistent when the optimization outcome conforms to the true robot trajectory and the topology of the environment [6]. In fact, convergence to the globally optimal solution to a SLAM problem is not guaranteed by the widely prevalent iterative optimization-based SLAM solvers [3], [7]. To that end, researchers have started to investigate the nature of the SLAM problem to profoundly understand its structure back in 2010 [8].…”

Section: Introductionmentioning

confidence: 99%

Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM

et al. 2021

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The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called PoseConv, to perform classification of posegraphs as optimal or sub-optimal. The operation of PoseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model's capability to perform classification with 92−98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators.

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“…Consequently, these advantages make UGVs suitable for indoor 4,5 and outdoor 6,7 applications. However, the ground vehicles can be perturbed due to uncertainties in models, and description of the context of operation 8 , skidding and slipping 9 , noisy sensor's measurements, 10 , and failures in electromechanical components 11 , making the navigation and control of the platform a challenging task 12,13 . Motion planning and control are the two crucial factors for a vehicle to complete an assigned task safely and effectively 14 .…”

Section: Introductionmentioning

confidence: 99%

Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

Khan¹,

Guivant²

2021

Preprint

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This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

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Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Cited by 80 publications

References 276 publications

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM

Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

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