A bat-pigeon algorithm to crack detection-enabled autonomous vehicle navigation and mapping

Lei, Tingjun; Luo, Chaomin; Sellers, Timothy; Rahimi, Shahram

doi:10.1016/j.iswa.2021.200053

Cited by 21 publications

(16 citation statements)

References 32 publications

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“…Many approaches have been proposed to achieve reliable autonomous robot motion planning, such as ant colony optimization (ACO) [11][12][13] , fireworks algorithm (FWA) [14] , bat-pigeon algorithm (BPA) [15] , graphbased method [16] , and neural network models [17][18][19] . Lei et al proposed a hybrid model to optimize the trajectory of the global path using a graph-based search algorithm associated with an ant colony optimization (ACO) method [11] .…”

Section: Related Workmentioning

confidence: 99%

“…The cuckoo search algorithm has also been successfully applied to the efficient and safe navigation of robots [20] . A bat-pigeon algorithm [15] was developed with crack detection-driven autonomous vehicle navigation and mapping, in which a local search-based bat algorithm and a global search-based pigeon-inspired optimization algorithm are effectively integrated to improve the speed and performance of robot path planning and mapping. Wang and Meng [16] suggested a nonuniform sampling technique, which efficiently computes high-quality collision-free paths based on a generalized Voronoi graph.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An informative planning-based multi-layer robot navigation system as applied in a poultry barn

Lei¹,

Li²,

Luo³

et al. 2022

Intell Robot

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Many real-world robot applications, as found in precision agriculture, poultry farms, disaster response, and environment monitoring, require search, locate, and removal (SLR) operations by autonomous mobile robots. In such application settings, the robots initially search and explore the entire workspace to find the targets, so that the subsequent robots conveniently move directly to the targets to fulfill the task. A multi-layer robot navigation system is necessary for SLR operations. The scenario of interest is the removal of broiler mortality by autonomous robots in poultry barns in this paper. Daily manual collection of broiler mortality is time- and labor-consuming, and an autonomous robotic system can solve this issue effectively. In this paper, a multi-layer navigation system is developed to detect and remove broiler mortality with two robots. One robot is assigned to search a large-scale workspace in a coverage mode and find and locate objects, whereas the second robot directly moves to the located targets to remove the objects. Directed coverage path planning (DCPP) fused with an informative planning protocol (IPP) is proposed to efficiently search the entire workspace. IPP is proposed for coverage directions in DCPP devoted to rapidly achieving spatial coverage with the least estimation uncertainty in the decomposed grids. The detection robot consists of a developed informative-based directed coverage path planner and a You Only Look Once (YOLO) V4-based dead bird detector. It refines and optimizes the coverage path based on historical data on broiler mortality distribution in a broiler barn. The removal robot collects dead broilers driven by a new hub-based multi-target path routing (HMTR) scheme, which is applicable to row-based environments. The proposed methods show great potential to navigate in broiler barns efficiently and safely, thus being a useful component for robotics. The effectiveness and robustness of the proposed methods are validated through simulation and comparison studies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An informative planning-based multi-layer robot navigation system as applied in a poultry barn

Lei¹,

Li²,

Luo³

et al. 2022

Intell Robot

View full text Add to dashboard Cite

show abstract

“…Another challenging problem that arises in CCPP is obstacle avoidance ( An et al, 2018 ; Wang et al, 2021 ). Based on the excellent optimization and search capabilities of nature-inspired algorithms, researchers have recently explored many nature-inspired computational approaches to solve vehicle collision-free navigation problems ( Deng et al, 2016 ; Ewerton et al, 2019 ; Lei et al, 2019 , 2021 ; Segato et al, 2019 ). For instance, a hybrid fireworks algorithm with LIDAR-based local navigation was developed by Lei et al (2020a) , capable of generating short collision-free trajectories in unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

et al. 2022

Self Cite

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With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an obstacle fusion paradigm in the map is developed based on the unmanned aerial vehicle (UAV) onboard sensors. A nature-inspired algorithm is adopted for obstacle avoidance and CCPP re-joint. Equipped with the onboard LIDAR equipment, autonomous vehicles, in the third layer, dynamically avoid moving obstacles. Simulated experiments validate the effectiveness and robustness of the proposed framework.

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“…In order to employ robotic systems in real-world scenarios, one critical factor is to develop autonomous robot multi-waypoint navigation and mapping system [17] . In order to solve the autonomous robot navigation problem, countless algorithms have been developed, such as graph-based [18,19] , ant colony optimization (ACO) [20][21][22] , bat-pigeon algorithm (BPA) [23] , neural networks [24][25][26] , fuzzy logic [27] , artificial potential field (APF) [28] , sampling-based strategy [14,29] , hybrid algorithms [30] , task planning algorithm [31] , etc. Chen et al produced a hybrid graph-based reinforcement learning architecture to develop a method for robot navigation in crowds [18] .…”

Section: Introductionmentioning

confidence: 99%

“…Luo et al proposed an improved vehicle navigation method, which utilizes a heading-enabled ACO algorithm to improve trajectory towards the target [20] . Lei et al developed a Bat-Pigeon algorithm with the ability to adjust the speed navigation of autonomous vehicles [23] . Luo et al developed the model for multiple robots complete coverage navigation while using a bio-inspired neural network to dynamically avoid obstacles [32] .…”

Section: Introductionmentioning

confidence: 99%

A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Sellers¹,

Lei²,

Luo³

et al. 2022

Intell Robot

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Autonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved -spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.

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A bat-pigeon algorithm to crack detection-enabled autonomous vehicle navigation and mapping

Cited by 21 publications

References 32 publications

An informative planning-based multi-layer robot navigation system as applied in a poultry barn

An informative planning-based multi-layer robot navigation system as applied in a poultry barn

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

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