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.

In this paper, a force and torque controller for aerial manipulation is developed using an unmanned aerial vehicle equipped with a robotic arm to interact near or on a vertical surface such as a wall. Control of aerial manipulators interacting with the environment is a challenging task due to dynamic interactions between aerial vehicles, robotic arms, and environment. To achieve this, modeling of aerial manipulators is first investigated and presented considering interaction with the environment. Nonlinear models of generic aerial manipulators, as well as of a prototype aerial manipulator composed of a hexacopter with a three-joint robotic arm, are established. An equilibrium-based force and torque controller is developed to conduct tasks that require the aerial manipulator to exert forces and torques on a wall. Simulations and experiments validate the performance of the controller that successfully applies desired forces and torques to an object fixed on a wall while flying near the wall.

This paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

Navigation tasks are often subject to several constraints that can be related to the sensors (visibility) or come from the environment (obstacles). In this paper, we propose a framework for autonomous omnidirectional wheeled robots that takes into account both collision and occlusion risk, during sensor-based navigation. The task consists in driving the robot towards a visual target in the presence of static and moving obstacles. The target is acquired by fixed – limited field of view – on-board cameras, while the surrounding obstacles are detected by lidar scanners. To perform the task, the robot has not only to keep the target in view while avoiding the obstacles, but also to predict its location in the case of occlusion. The effectiveness of our approach is validated through several experiments.

Regrasping is a manipulation to alternate between grasp configurations of an object to perform different tasks. We address a regrasping problem termed longitude regrasping to reposition a gripper along an elongated object. We propose an algorithm using the dynamics of the arm and a non-dexterous gripper to perform the manipulation. Energy control is used to toss the object up and catch it under the goal position. Clipped Linear Quadratic Regulator control approach is then applied to the gripper jaws to control the friction force on the object and to let it slide to the final goal position. The object sliding within the gripper is modeled as a semi-active linear joint where only dissipative forces can be applied to it. A set of experiments validated the feasibility of the method.

During visual servoing space activities, the attitude of free-floating space robot may be disturbed due to dynamics coupling between the satellite base and the manipulator. And the disturbance may cause communication interruption between space robot and control center on earth. However, it often happens that the redundancy of manipulator is not enough to fully eliminate this disturbance. In this paper, a method named off-line optimizing visual servoing algorithm is innovatively proposed to minimize the base disturbance during the visual servoing process where the degrees-of-freedom of the manipulator is not enough for a zero-reaction control. Based on the characteristic of visual servoing process and the robot system modeling, the optimal control method is applied to achieve the optimization, and a pose planning method is presented to achieve a second-order continuity of quaternion getting rid of the interruption caused by ambiguity. Then simulations are carried out to verify the method, and the results show that the robot is controlled with optimized results during visual servoing process and the joint trajectories are smooth.

Industrial applications that involve working on and moving a heavy load or that constrain the operator to work in uncomfortable positions can take advantage of the assistance of a robotic assistant. In this paper, we propose an architecture for an ergonomic human–robot co-manipulation of objects of various shapes and weight. The object is carried by the robot and, thanks to an ergonomic planner, is positioned in the most comfortable way for the user. Furthermore, thanks to an admittance control with payload compensation, the user can easily adjust the position of the object for working on different parts of it. The proposed architecture is experimentally validated in a robotic cell including an ABB industrial robot.

Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

Biological fish can create high forward swimming speed due to change of thrust/drag area of pectoral fins between power stroke and recovery stroke in rowing mode. In this paper, we proposed a novel type of folding pectoral fins for the fish robot, which provides a simple approach in generating effective thrust only through one degree of freedom of fin actuator. Its structure consists of two elemental fin panels for each pectoral fin that connects to a hinge base through the flexible joints. The Morison force model is adopted to discover the relationship of the dynamic interaction between fin panels and surrounding fluid. An experimental platform for the robot motion using the pectoral fin with different flexible joints was built to validate the proposed design. The results express that the performance of swimming velocity and turning radius of the robot are enhanced effectively. The forward swimming velocity can reach 0.231 m/s (0.58 BL/s) at the frequency near 0.75 Hz. By comparison, we found an accord between the proposed dynamic model and the experimental behavior of the robot. The attained results can be used to design controllers and optimize performances of the robot propelled by the folding pectoral fins.

In order to make the end of the three-axis platform follow the control command and achieve stable control of the end attitude, an improved orientation vector spherical linear interpolation (SLERP) method is proposed for the requirements, which specifically handles the position of the gimbal lock, so that the platform can move smoothly around the gimbal lock position. A three-axis platform with a camera at the end is set up for the validity of the proposed algorithm. At first, an adaptive speed measurement method based on incremental encoder is introduced, which can automatically adapt to high and low speed, and estimate the ultra-low speed to realize the speed measurement of large dynamic range, and this is used for the motion control of the three-axis platform. Then, the SLERP method for the quaternion interpolation on the starting and ending attitudes represented in quaternion is introduced in detail, and it is continuously improved in response to its existing problems for the platform. Finally, an orientation vector SLERP method is proposed, which uses viscosity factor and rejection factor to adjust the algorithm near the platform’s gimbal lock position. A tracking experiment was designed using the red ball as the following target detected by the designed target tracking algorithm using the camera, which verified the effectiveness of the attitude tracking control based on the proposed improved orientation vector SLERP.

In recent years, soft robotics is widely considered as the most promising field for both research and application. First of all, the actuator is fundamental for designing, modeling, and controlling of soft robots. This paper presents a new type of pneumatic trunk-like soft actuator, which contains a chamber for stiffness adjustment in addition to three chambers for driving. Thus, the salient feature of the proposed actuator is the ability of stiffness self-regulation. The structure of the proposed actuator is described in detail. Then the theoretical models for elongation and bending motion of the actuator are established. The elongation as well as single-chamber and multi-chamber driving bending of the actuator were tested to verify the mathematical models. Finally, a dual-segment soft robot based on the proposed trunk-like soft actuator was developed and tested by experiments, which implies its potential application in practice.

Against the backdrop of accelerated ageing around the globe, an increasing number of individuals suffer from hip motion disability and gait disorders. In this paper, the performance analysis of a novel parallel assistive mechanism with 2 DOF for hip adduction/abduction (AB/AD) and flexion/extension (FL/EX) assistance is completed and evaluated, particularly the velocity and force transfer features. The analysis shows that the assistive mechanism has advantages of fine motion assistive isotropy, high force transfer ratio and large force isotropic radius, which indicates that the parallel assistive mechanism is suitable for hip AB/AD and FL/EX assistance.