Receding-horizon RRT-Infotaxis for autonomous source search in urban environments

An, Seulbi; Park, Minkyu; Oh, Hyondong

doi:10.1016/j.ast.2021.107276

Cited by 22 publications

(7 citation statements)

References 49 publications

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“…As for the local planning phase, smooth and collision-free paths toward each waypoint are generated while maintaining the perception quality in the 3D local map. Owing to the limitation of the LiDAR’s scanning range, the local planning is conducted based on the current pose and local map at each step, that is, the problem is solved in a receding horizon manner as in Tang et al 28…”

Section: Problem Statementmentioning

confidence: 99%

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Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Takemura

Aoki

Ishigami

2023

Advances in Mechanical Engineering

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This paper presents an energy and perception aware framework for path planning and navigation of unmanned aerial vehicles (UAVs) in GNSS-denied and spatiotemporal wind environments. The proposed framework mainly consists of the global and local path planning methods that respectively consider the energy consumption of an UAV and perception quality of a light detection and ranging (LiDAR) sensor mounted on the UAV. The energy consumption is estimated based on the aerodynamic model that calculates drag and lift forces on the UAV. The global planner then uses the total energy consumption in the spatiotemporal wind as the cost function to find an energy-efficient path as a set of waypoints. The local path planning navigates the UAV between the waypoints with maintaining the perception quality. The perception quality is quantified based on how well the LiDAR sensor scans feature points around the UAV that highly correlates with the navigation accuracy. Numerical simulation study for each of the global and local path planners validates their usefulness. Further, the overall framework is entirely verified in a long-range flight scenario of the photorealistic environments developed in the Gazebo simulation.

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Section: Problem Statementmentioning

confidence: 99%

“…In this study, RRT* extends the tree as a library of candidate paths. This approach is similar to, 28 where a UAV efficiently explored unknown areas. Given that the generated tree may not be smooth for the UAV's tracking, dynamic models 11 or smoothing techniques 35,36 are necessary.…”

Section: Tree Generationmentioning

confidence: 99%

Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Takemura

Aoki

Ishigami

2023

Advances in Mechanical Engineering

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“…Recently, An et al present an urban source search algorithm namely receding-horizon RRT-Infotaxis (An et al, 2022). This work leverages the standard RRT path planning technique along with the Infotaxis method of determining future sample location utility (similar in framework to our BIT * and Entrotaxis technique).…”

Section: Source Search In Complex Environmentsmentioning

confidence: 99%

Autonomous search of an airborne release in urban environments using informed tree planning

et al. 2022

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The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

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“…Algorithm is the core of robot path planning [3]. The path-planning algorithm can be roughly classified as (1) path-planning algorithms based on map search, such as the A-star algorithm [4][5][6], artificial potential field algorithm [7,8], etc., (2) samplingbased path-planning algorithms, such as the RRT algorithm [9][10][11], PRM algorithm [12], etc., and (3) swarm intelligence algorithms based on global optimization, such as the ant colony optimizer [13][14][15], artificial bee colony optimizer [16,17], algorithms based on deep learning [18][19][20], etc.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

Zhao,

Cai,

Wang

et al. 2024

Applied Sciences

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This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions. In the path-planning experiment, the length, number and size of inflection points and stability of the path planned by the GSGWO are better than those of the GWO. The feasibility of the GSGWO is verified.

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Receding-horizon RRT-Infotaxis for autonomous source search in urban environments

Cited by 22 publications

References 49 publications

Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Energy-and-perception-aware planning and navigation framework for unmanned aerial vehicles

Autonomous search of an airborne release in urban environments using informed tree planning

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

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