Humanoid Robot Framework for Research on Cognitive Robotics

Perico, Danilo H.; Homem, Thiago Pedro Donadon; Almeida, Aislan C.; Silva, Isaac J.; Vilao, Claudio O.; Ferreira, Vinicius N.; Bianchi, Reinaldo A. C.

doi:10.1007/s40313-018-0390-y

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…Experiments were executed in a Humanoid Robot 2D Simulator proposed in Perico et al (2018) that allows portability between simulated and real robots.…”

Section: Tests In the Simulatormentioning

confidence: 99%

Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning

Perico,

Santos,

Bianchi

2020

Preprint

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Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localisation and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location. In order to accomplish this, the present paper assumes relations from the qualitative spatial reasoning formalism called StarVars, whose inference method is also used to build a model of the domain. This paper presents two qualitative-probabilistic algorithms for guided navigation using a particle filter and qualitative spatial relations. In the first algorithm, the particle filter is run upon a qualitative representation of the domain; whereas, the second algorithm transforms the numerical output of a standard particle filter into qualitative relations to guide a sensory deprived robot. The proposed methods were evaluated with experiments carried out on a 2D humanoid robot simulator. A proof of concept executing the algorithms on a group of real humanoid robots is also presented. The results obtained demonstrate the success of the guided navigation models proposed in this work.

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“…Experiments were executed in a Humanoid Robot 2D Simulator proposed in Perico et al (2018) that allows portability between simulated and real robots.…”

Section: Tests In the Simulatormentioning

confidence: 99%

Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning

Perico,

Santos,

Bianchi

2020

Preprint

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“…As described in the introduction, the two key factors of cognitive robotics, are knowledge representation and cognitive reasoning [3]. Although the problem of improving autonomy is non-trivial, it is relevant to a variety of robotic applications, for example, in humanoid robotics [4,5], human-robot interaction [6][7][8][9], Search and Rescue (SAR) [10] and multi-robot systems [11].…”

Section: Related Workmentioning

confidence: 99%

A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019

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High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropybased fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.

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“…Researchers are also exploring framework-based approaches to construct generalized systems for different applications. In one study, researchers proposed an open-source framework for a humanoid robot using cross architecture, which was low-cost in computation [ 28 ]. The framework was validated via both simulator and telemetry interface, and the result showed that it could be used to design new algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

Shahria¹,

Sunny²,

Zarif³

et al. 2022

JMIR Biomed Eng

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Background Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.

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Humanoid Robot Framework for Research on Cognitive Robotics

Cited by 13 publications

References 11 publications

Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning

Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning

A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

A Novel Framework for Mixed Reality–Based Control of Collaborative Robot: Development Study

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