Active Vision via Extremum Seeking for Robots in Unstructured Environments: Applications in Object Recognition and Manipulation

Çallı, Berk; Caarls, Wouter; Wisse, Martijn; Jonker, Pieter

doi:10.1109/tase.2018.2807787

Cited by 30 publications

(24 citation statements)

References 59 publications

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“…In order to sense the object’s feature, the first step is to search and localize the object. As showed in Table 3 , the reviewed approaches are classified into three categories, which are image-based approaches [ 107 , 108 ], point cloud-based approaches [ 109 ], and tactile perception-based approaches [ 110 , 111 , 112 ]. The first two approaches are relatively mature and could deal with the problem of multi-object search in a cluttered environment.…”

Section: Resultsmentioning

confidence: 99%

“…The first two approaches are relatively mature and could deal with the problem of multi-object search in a cluttered environment. The POMDP [ 108 ], extremum seeking strategy [ 107 ], and learning networks [ 109 ] are usually used in these approaches and the results are acceptable to a certain extent. However, these methods require a higher quality of images and point clouds.…”

Section: Resultsmentioning

confidence: 99%

“…The first approach is that the object is detected and segmented from the 2D image, and the part registration of the object is generated with the viewpoint identification. Based on Extremum Seeking Control [ 107 ], the active vision strategy is able to conduct continuous optimization backed up with mechanisms to escape from local maxima. For the clutter environment, as shown in Figure 12 , the Partially Observable Markov Decision Process (POMDP) [ 108 ] is used to solve the problem of object searching.…”

Section: Unknown Objectsmentioning

confidence: 99%

See 2 more Smart Citations

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Unknown Objectsmentioning

confidence: 99%

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Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

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“…To facilitate the transition, Sim2Real learning methods can be used [230]. Finally, it is also possible to use real equipment and environments [224], [231], but such training is slow and requires having access to suitable equipment. Therefore, training setups are typically simplistic.…”

Section: A Single-agent Active Perceptionmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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“…When faced with the design of motion strategies for autonomous robotic systems, these sensors prove to be very convenient, and even fundamental [5,6]. When environments are dynamic (a typical problem for service robots) it is necessary for the robot to be able to identify nearby obstacles in real time [7,8].…”

Section: Introductionmentioning

confidence: 99%

Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

Sarmiento¹,

Jacinto²,

Martínez³

2020

IJECE

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This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.

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Active Vision via Extremum Seeking for Robots in Unstructured Environments: Applications in Object Recognition and Manipulation

Cited by 30 publications

References 59 publications

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

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