Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi, Dayal R.

doi:10.15406/iratj.2018.04.00109

Cited by 8 publications

(4 citation statements)

References 78 publications

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“…In particular, active inference encompasses epistemic inference, which involves reducing prediction errors by updating beliefs through sensory inputs from active observation of current situations, and instrumental inference that involves reducing prediction errors by selecting actions to change current situations [69]. For example, commercially-available mobile industrial robot (MIR) platforms have sufficiently advanced narrow AI to move around a factory and learn while doing so [70]. MIR can make predictions of the shortest route for the delivery of materials in a factory.…”

Section: Active Inference As a Unifying Frameworkmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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Section: Active Inference As a Unifying Frameworkmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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“…Algorithm optimisation is widely used to speed-up computation and reduce power consumption (Parhi 2018), however, it is often not sufficient on its own due to sheer volume of information and the speed with which robots are expected to react to events, especially in dependable and safety critical scenarios (Gouveia et al 2014;Chen et al 2018).…”

Section: Multi-robot Systemsmentioning

confidence: 99%

Reliable distribution of computational load in robot teams

2021

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Modern multi-robot systems often need to solve computationally intensive tasks but operate with limited compute resources and in the presence of failures. Cooperating to share computational tasks between robots at the edge reduces execution time. We introduce and evaluate a new computation load management technology for teams of robots: Reliable Autonomous Mobile Programs (RAMPs). RAMPs use information about the computational resources available in the team and a cost model to decide where to execute. RAMPs are implemented in ROS on a collection of Raspberry Pi-based robots. The performance of RAMPs is evaluated using route planning, a typical computationally-intensive robotics application. A systematic study of RAMPs demonstrates a high likelihood of optimal or near-optimal distribution and hence efficient resource utilisation. RAMPs successfully complete in the presence of simultaneous, or successive, robot failures and network failures, while preserving near-optimal distribution.

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“…Various navigational techniques have been employed on multiple robots by researchers. 1–10 Pradhan et al 11 have examined the route outlining of multiple robots by the use of the Neuro-Fuzzy approach. The travel length and travel time are observed to accumulate with the conventional approach via a rule-based approach.…”

Section: Introductionmentioning

confidence: 99%

Route outlining of humanoid robot on flat surface using MFO aided artificial potential field approach

Kashyap

Parhi

Kumar

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target; when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique.

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Advancement in navigational path planning of robots using various artificial and computing techniques

Cited by 8 publications

References 78 publications

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Reliable distribution of computational load in robot teams

Route outlining of humanoid robot on flat surface using MFO aided artificial potential field approach

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