Optimal camera placement considering mobile robot trajectory

Nikolaidis, Stefanos; Ueda, Ryuichi; Hayashi, Atsushi; Arai, T.

doi:10.1109/robio.2009.4913204

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…Autonomous patrolling robots are to have significant contributions in security applications for surveillance purposes (Briggs and Donald 2000;Cassinis and Tampalini 2007). In the near future robots will also be used in home environments to provide assistance for the elderly and challenged people (Nikolaidis et al 2009;Biegelbauer et al 2010).…”

Section: Surveillance With Mobile Robotsmentioning

confidence: 99%

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

368

204

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In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.

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Section: Surveillance With Mobile Robotsmentioning

confidence: 99%

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

368

204

View full text Add to dashboard Cite

show abstract

“…While we have assumed full observability of the scene, we plan to account for sensing limitations in the future [29], [30]. Future work also includes using learning to improve the planning efficiency [4] and extending the developed techniques to more general MR-TAMP problems [3] and more diverse environments [31], [32].…”

Section: Discussionmentioning

confidence: 99%

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Zhang¹,

Chan²,

Zhong³

et al. 2022

Preprint

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We address multi-robot geometric task-andmotion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot as well as information about whether two robots can perform a handover action by calling motionplanning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-andmotion plans. The search process for collaborative task-andmotion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging GTAMP domains and show that it can generate high-quality task-andmotion plans with respect to the planning time, the resulting plan length and the number of objects moved compared to two state-of-the-art baselines.

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“…In particular, we: explained the need for precise robot localization at a home environment and verified the importance of the camera pose at the robot localization process divided the problem of optimal camera placement into two sub-objectives: a) place the cameras in order to maximize the area covered (single-objective case) b) place the cameras in order to maximize the area covered and minimize the average localization error of the visible area (multi-objective case) approached sub-objective (a) by a genetic algorithm and quantitively compared its performance with the steepest descent method used in past research [15]. approached sub-objective (b) by the NSGA genetic algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…We optimize the pose of cameras for the same evaluation criteria using the steepest descent method. The steepest descent method was used for the camera placement optimization problem in [15]. Results are included in Fig.…”

Section: The Environment Is a 490[m] X 455[m] X 23[m]mentioning

confidence: 99%

Optimal arrangement of ceiling cameras for home service robots using genetic algorithms

Nikolaidis

Arai

2009

RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication

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In the near future robots will be used in home environments to provide assistance for the elderly and challenged people. As home environments are complicated, external sensors like ceiling cameras need to be placed on the environment to provide the robot with information about its position. The pose of cameras influences the area covered by the cameras, as well as the error of the robot localization. We examine the problem of the finding the arrangement of ceiling cameras at home environments that maximizes the area covered and minimizes the localization error. Genetic algorithms are proposed for the single and multi-objective optimization problem. Simulation results indicate that we can obtain the optimal arrangement of cameras that satisfies the given objectives and the required constraints.

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Optimal camera placement considering mobile robot trajectory

Cited by 19 publications

References 8 publications

Active vision in robotic systems: A survey of recent developments

Active vision in robotic systems: A survey of recent developments

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Optimal arrangement of ceiling cameras for home service robots using genetic algorithms

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