Efficient cost computation in cost map planning for non-circular robots

King, Jennifer E.; Likhachev, Maxim

doi:10.1109/iros.2009.5354074

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…The cotsmap is a grid-based representation of the robot's environment where each cell has a cost associated with traversing through it. The cells that correspond with the position of obstacles are assigned higher costs, while free cells are given lower costs [40]. Also, the LiDAR data are integrated into the costmap_common_params.yaml file to specifically expand the robot's field of view.…”

Section: Costmap Configurationmentioning

confidence: 99%

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.

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Section: Costmap Configurationmentioning

confidence: 99%

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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“…One of the most interesting approaches that has been made, for the purposes of the problem addressed in this paper, is the utilization of two different occupancy maps: the original obstacles map and a second one inflated by the radius of a set of circles inscribed in the robot’s footprint [ 9 ]. The points of the footprint are then divided into two sets: points that should be evaluated against the cost map that has the inflated obstacles and points that should be evaluated against the original cost map.…”

Section: Related Workmentioning

confidence: 99%

Path Planning for Non-Circular, Non-Holonomic Robots in Highly Cluttered Environments

Samaniego¹,

López

Vázquez

2017

Sensors

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This paper presents an algorithm for finding a solution to the problem of planning a feasible path for a slender autonomous mobile robot in a large and cluttered environment. The presented approach is based on performing a graph search on a kinodynamic-feasible lattice state space of high resolution; however, the technique is applicable to many search algorithms. With the purpose of allowing the algorithm to consider paths that take the robot through narrow passes and close to obstacles, high resolutions are used for the lattice space and the control set. This introduces new challenges because one of the most computationally expensive parts of path search based planning algorithms is calculating the cost of each one of the actions or steps that could potentially be part of the trajectory. The reason for this is that the evaluation of each one of these actions involves convolving the robot’s footprint with a portion of a local map to evaluate the possibility of a collision, an operation that grows exponentially as the resolution is increased. The novel approach presented here reduces the need for these convolutions by using a set of offline precomputed maps that are updated, by means of a partial convolution, as new information arrives from sensors or other sources. Not only does this improve run-time performance, but it also provides support for dynamic search in changing environments. A set of alternative fast convolution methods are also proposed, depending on whether the environment is cluttered with obstacles or not. Finally, we provide both theoretical and experimental results from different experiments and applications.

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“…O mapa de custos é utilizado para determinar o trajeto de navegação (planejamento da trajetória), a qual será o sinal de referência para o controlador de navegação. A trajetória é especificada de acordo com os custos do mapa, buscando sempre o trajeto de menor custo [13].…”

Section: Navegação E Mapeamentounclassified

Navegação e Mapeamento Simultâneo em um Carro Autônomo Baseado na Lógica Nebulosa

Filho¹,

Oliveira²,

Oliveira³

et al. 2015

Anais Do 12. Congresso Brasileiro De Inteligência Computacional

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Resumo-Este trabalho apresenta o controle de navegação e mapeamento de um carro autônomo por sistema nebuloso (Fuzzy) que permite o desvio automático de obstáculos em ambientes desconhecidos. A estratégia baseia-se em um mapa do ambiente, que é criado de acordo com a navegação, para planejar as trajetórias através do algoritmo de busca A*. A abordagem proposta é avaliada em um ambiente virtual, onde o carro autônomo deve movimentar-se entre diversos obstáculos. Palavras-chave-Carro autônomo, lógica nebulosa, mapeamento automático de obstáculos, algoritmo A*.

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Efficient cost computation in cost map planning for non-circular robots

Cited by 13 publications

References 11 publications

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)

Path Planning for Non-Circular, Non-Holonomic Robots in Highly Cluttered Environments

Navegação e Mapeamento Simultâneo em um Carro Autônomo Baseado na Lógica Nebulosa

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