Multi-Agent formation control with target tracking and navigation

Liu, Lebin; Luo, Chaomin; Shen, Furao

doi:10.1109/icinfa.2017.8078889

Cited by 27 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to avoid dynamic and unknown obstacles, the proposed model employs the Vector Field Histogram (VFH) model as a reactive local navigator. An autonomous robot uses a velocity command to and from each waypoint, provided by the local navigator [41] . By applying the VFH to the overall global trajectory with a sequence of markers, the path can be broken down into various segments to improve the efficiency in obstaclepopulated workspaces.…”

Section: Reactive Local Navigationmentioning

confidence: 99%

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

Sellers¹,

Lei²,

Luo³

et al. 2022

Intell Robot

View full text Add to dashboard Cite

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.

show abstract

Section: Reactive Local Navigationmentioning

confidence: 99%

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

Sellers¹,

Lei²,

Luo³

et al. 2022

Intell Robot

View full text Add to dashboard Cite

show abstract

“…However, robots for poultry mortality removal are still not commercially available. Many aspects need to be considered for developing such a robot [8] . Among them, robot path planning is one of the most essential parts of effective and efficient determination of robot routes [9,10] .…”

Section: Introductionmentioning

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

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.

show abstract

“…In a pursuit-evasion game, several processes such as target tracking, formation control, and obstacle avoidance interact to form an interconnected system. Extreme seeking control (ESC) is control method that can be used in a pursuit-evasion game to perform target tracking and then form the desired formation, as investigated in [15]. This method uses the scientific basis of finding the optimal point to control the formation of a multi-agent system, but the method in this study can still be developed because the dynamic model of the controlled agent is very simple, that is, by assuming that the controlled agent is a moving point.…”

Section: Introductionmentioning

confidence: 99%

Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game

Setiawan

Agustinah

Fuad

2022

JAREE

View full text Add to dashboard Cite

In a pursuit-evasion game, the mobile robot pursuer's ability to navigate from its initial position to the evader while maintaining a safe distance from other objects requires a good obstacle avoidance system. This study aims to perform target tracking in evader sieges and obstacle avoidance against other pursuer robots and static obstacles by proposing a modified extreme seeking controller (ESC). A modified backstepping control (BC) was used as an autopilot control for a nonholonomic mobile robot to execute the modified ESC command. The modified BC based on the modified ESC requires the positions of the targeted evader, pursuers, and obstacles. The pursuer uses this information to capture an evader by arranging the desired formation without colliding with static obstacles or other robots. The results of the simulations show that the pursuers successfully surround the evader and construct the formation without colliding with obstacles. The proposed method resulted in the closest distance of 2.071 m between the pursuers, 1.954 m between each pursuer and the evader, and 2.425 m between the pursuers and static obstacles.

show abstract

Multi-Agent formation control with target tracking and navigation

Cited by 27 publications

References 13 publications

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

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

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

Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game

Contact Info

Product

Resources

About