Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Muni, Manoj Kumar; Parhi, Dayal R.; Kumar, Priyadarshi Biplab

doi:10.1017/s0263574720000235

Cited by 19 publications

(9 citation statements)

References 34 publications

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“…Moreover, the technology of deep learning [43][44][45][46] should also be considered to build machine aesthetic models. Furthermore, some advanced motion planning algorithms of humanoid robots [47,48] will be helpful to generate robotic dance motions quickly, avoid robots falling to a certain extent, and finally enrich the construction of the dataset of robotic dance motions. These open questions will be explored in the future.…”

Section: Limitation Of the Proposed Approachmentioning

confidence: 99%

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Peng

Wang

et al. 2021

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Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

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Section: Limitation Of the Proposed Approachmentioning

confidence: 99%

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Peng

Wang

et al. 2021

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“…It may be seen in the prey behavior of social insects, the navigation of herds of birds, and the careful activity of fishes.Swarm intelligence refers to an animal's collective and self-organizing behavior. Several publications have employed bio-inspired methodologies to tackle various parts of path planning strategies [1][2][3][4][5][6][7][8][9], a Whale Optimization Algorithm (WOA), applied in fixed situations to meet prerequisites for the optimization length of the path and smoothing path [10,11], and Dai et al [12], suggested a method that based on the Cuckoo Optimization Algorithm for planning the robot's path in a moving situation.…”

Section: Introductionmentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

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In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

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“…The goals of optimization involved are the length of the path, the degree of evenness and the degree of safety of the route. Muni et al (2020a, 2020b, 2020c, 2020d, 2021) have proposed the hybrid artificial intelligent technique for control over humanoid robots. Kumar et al (2020a, 2020b, 2021) have proposed hybrid sine cosine and ACO technique path planning of mobile robot.…”

Section: Introductionmentioning

confidence: 99%

“…The goals of optimization involved are the length of the path, the degree of evenness and the degree of safety of the route. Muni et al (2020aMuni et al ( , 2020bMuni et al ( , 2020cMuni et al ( , 2020dMuni et al ( , 2021 have proposed the hybrid artificial intelligent technique for control over humanoid robots. Kumar et al (2020aKumar et al ( , 2020bKumar et al ( , 2021 Several techniques studied in the literature survey are already proposed by the researchers concerning navigational problems of the mobile robot, but no one confirms the generated path is optimal or not.…”

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confidence: 99%

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

Paital

Kumar

Muni

et al. 2021

IJIUS

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PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

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Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Cited by 19 publications

References 34 publications

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

A review: On bio-inspired optimization methods for path planning of mobile robot

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

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