In the current investigation, a two-stage hybridization model has been used for the motion planning of humanoids in complex environmental conditions using regression analysis and the firefly algorithm. In the first step, sensory outputs are fed to the regression model, and an initial turning angle (ITA) is obtained. In the second step, the ITA is again fed as input to the firefly model along with other required inputs, and the final turning angle (FTA) is obtained. The FTA serves as the guiding parameter for the humanoids to reach their desired destinations. The developed motion planning scheme has been implemented on NAO humanoid robots in simulation and experimental platforms. A Petri-Net control strategy has been integrated along with the hybrid scheme while negotiating multiple humanoids in a common platform. The results obtained from the motion planning analysis in simulation and experimental arenas are compared against each other in terms of selected navigational parameters and observed satisfactory agreements. Finally, the proposed hybrid controller is also tested against another standard navigational model and substantial enhancement in the performance has been noticed.

In this paper, an improved adaptive motion-force control approach is introduced to control the cooperative manipulators transporting a shared object under limited communication. The adaptive controller is designed based on the backstepping approach to control the motion of the handled object in the presence of uncertainties and external disturbances. Moreover, the force controller is established to maintain constant internal forces. An event-triggered (ET) mechanism is derived based on the Lyapunov analysis to deal with the bandwidth restrictions and maintain the system stability during the cooperative manipulation. The effectiveness of the proposed control scheme is investigated by comparing it with the existing variations of adaptive backstepping control (i.e., traditional and state augmented schemes). Moreover, the designed triggering mechanism is compared with different triggering conditions presented in the literature. The proposed control approach is further validated in a more realistic virtual robot experimentation platform (i.e., V-REP) using two SCORBOT-ER VPlus manipulators. From the TrueTime-based simulation runs, the proposed control scheme exhibits superior performance in tandem with efficient utilization of the network resources during the transportation task.

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.

In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions.

In the manufacturing process of sophisticated and individualized large components, classical solutions to build large machine tools cannot meet the demand. A hybrid robot, which is made up of a 3 degree-of-freedom (3-DOF) parallel manipulator and a 2-DOF serial manipulator, has been developed as a plug-and-play robotized module that can be rapidly located in multi-stations where machining operations can be performed in situ. However, processing towards high absolute accuracy has become a huge challenge due to the movement of robot platform. In this paper, a human-guided vision system is proposed and integrated in the robot system to improve the accuracy of the end-effector of a robot. A handheld manipulator is utilized as a tool for human–robot interaction in the large-scale unstructured circumstances without intelligence. With 6-DOF, humans are able to manipulate the robot (end-effector) so as to guide the camera to see target markers mounted on the machining datum. Simulation is operated on the virtual control platform V-Rep, showing a high robust and real-time performance on mapping human manipulation to the end-effector of robot. And then, a vision-based pose estimation method on a target marker is proposed to define the position and orientation of machining datum, and a compensation method is applied to reduce pose errors on the entire machining trajectory. The algorithms are tested on V-Rep, and the results show that the absolute pose error reduces greatly with the proposed methods, and the system is immune to the motion deviation of the robot platform.

In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

With an ability to mimic the human behaviour and replace human efforts in proper platforms, humanoid robots have always acquired a special place among robotics practitioners. Being a complex method of analysis, navigation and path planning, humanoid robots still possess an interesting yet challenging area of investigation. In the current work, a novel navigational strategy has been proposed for smooth and hassle-free movement of single as well as multi-humanoid robots in complex environments. Here, the navigational plan is based on a virtual target displacement strategy which is activated when the robot is unable to find a safe path along the actual target line. After detection of a potential obstacle by the sensors of the robot, a number of virtual targets are generated around the actual target. Then, the most feasible path and point to move are calculated by assigning suitable weightage through several selected parameters to each target line and visualizing the safest path. The proposed approach is implemented on a V-REP simulation platform, and the simulation results are also validated against an experimental set-up prepared under test conditions. The validation of simulation results against experimental counterparts has revealed satisfactory agreement between them. To avoid possibility of any inter-collision during navigation of multi-humanoids under a common platform, a Petri-Net strategy has been integrated along with the proposed control strategy. Finally, the developed approach is also assessed against another existing navigational controller, and a significant performance improvement has been observed.