Autonomous indoor robot navigation using a sketch interface for drawing maps and routes

Boniardi, Federico; Valada, Abhinav; Burgard, Wolfram; Tipaldi, Gian Diego

doi:10.1109/icra.2016.7487453

Cited by 47 publications

(22 citation statements)

References 18 publications

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“…For this reason, in long-term robot deployments to large office environments researchers have used manually designed topological graphs [12], [13]. Related map representations for long-term robot deployments include architectural floor plans [14], hand-drawn floor plans [15], sketches [16] and 3D vector maps [13]. Also for longterm planning, Probabilistic Roadmaps (PRMs) [6] have been used for legged robot locomotion planning [17] and in combination with Reinforcement Learning methods for longterm mobile robot navigation [18], [19].…”

Section: Related Work a Large Number Of Map Representations Have Beenmentioning

confidence: 99%

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

Brandão

Aladag

Havoutis

2020

IEEE Robot. Autom. Lett.

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Long-range locomotion planning is an important problem for the deployment of legged robots to real scenarios. Current methods used for legged locomotion planning often do not exploit the flexibility of legged robots, and do not scale well with environment size. In this paper we propose the use of navigation meshes for deployment in large-scale, potentially multi-floor sites. We leverage this representation to improve long-term locomotion plans in terms of success rates, path costs and reasoning about which gait-controller to use when. We show that NavMeshes have higher planning success rates than sampling-based planners for a fraction of the construction time (e.g. 2x success rate with 60x lower construction time), as well as finding 30% lower-cost paths, while this performance gap further increases when considering multi-floor environments. We present both a procedure for building controller-aware NavMeshes and a full navigation system that adapts to changes to the environment. We demonstrate the capabilities of the system in simulation experiments and in field trials at a realworld oil rig facility.

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Section: Related Work a Large Number Of Map Representations Have Beenmentioning

confidence: 99%

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

Brandão

Aladag

Havoutis

2020

IEEE Robot. Autom. Lett.

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“…One such task of navigation has seen tremendous progress over the years. Robots today have the capability to autonomously plan paths to reach a certain location and even make decisions based on the scene dynamics, avoiding collisions with people and objects (Boniardi et al, 2016 ; Gaydashenko et al, 2018 ; Jamshidi et al, 2019 ; Hurtado et al, 2020 ). Advancing robot navigation abilities is crucial for robots to effectively operate in real-world environments.…”

Section: Introductionmentioning

confidence: 99%

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

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“…For this, a WMR with monitor, cameras, and speakers was used to interact with the workspace, whereas a tablet was used to sketch routes and gestures for the WMR. Boniardi et al [17], [18] introduced an approach for localization and navigation based on hand-drawn sketches of the environment. The Monte Carlo algorithm, along with the local deformation of the sketch, was used to estimate the robot state.…”

Section: Introductionmentioning

confidence: 99%

Intuitive Planning, Generation, and Tracking of Trajectory for WMR With Mobile Computing Device and Embedded System

et al. 2020

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With the intention of delivering an intuitive and simple platform to plan, generate, and track trajectories with a WMR, this paper presents a technological integration of hardware and software. This technological integration consists of a mobile computing device with an app developed by authors for the trajectory planning and a differential drive WMR that possesses an embedded system, in which a trajectory generation algorithm and a trajectory tracking control algorithm-both proposed by authors-are programmed. The mobile computing device wirelessly drives the WMR through the embedded system. The app is developed on the basis of the sketch approach, since it allows drawing sketches of the trajectory intuitively on the mobile device. Also, the app geometrically scales the sketch coordinates to match them with the real WMR workspace. For the trajectory generation, these scaled coordinates are wirelessly sent to the embedded system, where the trajectory generation algorithm joins them by using Bézier polynomials and concatenation of straight-lines. Hence, the tracking task is carried out with the control algorithm, which is proposed departing from the dynamic model of the WMR and whose asymptotic stability proof is performed with the Lyapunov method. Experimental results verify that the technological integration successfully plans, generates, and tracks trajectories, even when the WMR is far from the initial coordinate of the desired trajectory. These results, corroborate the good performance and robustness of the proposed control algorithm. Thus, the introduced technological integration is intuitive and simple since users only have to draw a sketch on the mobile device to carry out the trajectory tracking task. INDEX TERMS Wheeled mobile robot, trajectory tracking task, trajectory planning, trajectory generation, sketch approach, Bézier polynomial, mobile computing device, embedded system, tracking control.

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Autonomous indoor robot navigation using a sketch interface for drawing maps and routes

Cited by 47 publications

References 18 publications

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

Intuitive Planning, Generation, and Tracking of Trajectory for WMR With Mobile Computing Device and Embedded System

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