Semantic Path Planning for Indoor Navigation and Household Tasks

Sun, Nico; Yang, Erfu; Corney, Jonathan; Chen, Leo

doi:10.1007/978-3-030-25332-5_17

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…There are also some integrated algorithms that create a ternary map (composed of fixed spaces, moving obstacles, and fixed obstacles). Robots operating in a human-interacting environment need to have a better knowledge of their environment in order to have autonomous navigation in terms of avoiding temporary obstacles that may intervene in their reach [ 33 ].…”

Section: Assistive Robotsmentioning

confidence: 99%

Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

Misaros

Stan

Donca

et al. 2023

Sensors

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It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.

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Section: Assistive Robotsmentioning

confidence: 99%

Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

Misaros

Stan

Donca

et al. 2023

Sensors

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“…Semantic navigation allows mobile robots to perform a semantic planning. As an example, Sun et al, recently presented a planning system for indoor navigation and household tasks [109]. The tasks implemented are move to region, move obstacles, clear a region of objects and moving objects to another region.…”

Section: Semantic Navigationmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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“…[ 9 ] have proposed BIM/IFC-based graph models for indoor robot navigation, and Sun et al. [ 10 ] have employed semantic information for path planning. Additionally, Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Chen

Liang

2023

Sensors

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Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot’s driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.

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Semantic Path Planning for Indoor Navigation and Household Tasks

Cited by 13 publications

References 12 publications

Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

Semantic Information for Robot Navigation: A Survey

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

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