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Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues.

Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup.

By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors.

Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

Smooth 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.

Therefore, 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.

During 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.

After 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.

This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments.

The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point.

Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these.

Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.