Robotic swimmers are currently a subject of extensive research and development for several underwater applications. Clever design and planning must rely on simple theoretical models that account for the swimmer’s hydrodynamics in order to optimize its structure and control inputs. In this work, we study a planar snake-like multi-link swimmer by using the “perfect fluid” model that accounts for inertial hydrodynamic forces while neglecting viscous drag effects. The swimmer’s dynamic equations of motion are formulated and reduced into a first-order system due to symmetries and conservation of generalized momentum variables. Focusing on oscillatory inputs of joint angles, we study optimal gaits for 3-link and 5-link swimmers via numerical integration. For the 3-link swimmer, we also provide a small-amplitude asymptotic solution which enables obtaining closed-form approximations for optimal gaits. The theoretical results are then corroborated by experiments and motion measurement of untethered robotic prototypes with three and five links floating in a water pool, showing a reasonable agreement between the experiments and the theoretical model.

The unique frictional properties conferred by snake ventral scales inspired the engineering and fabrication of surrogate mechanisms for a robotic snake. These artificial, biologically inspired scales produce anisotropic body-ground forcing patterns with various locomotion surfaces. The benefits they confer to robotic snake-like locomotion were evaluated in experimental trials employing rectilinear, lateral undulation, and sidewinding gaits over several distinct surface types: carpet, inhomogeneous concrete and homogeneous concrete. Enhanced locomotive performance, with respect to net displacement and heading stability, was consistently measured in scenarios that utilized the engineered scales, over equivalent scenarios where the anisotropic effects of scales were absent.

Robust positioning and navigation of a mobile robot in an urban environment is implemented by fusing the Global Positioning System (GPS) and Inertial Navigation System (INS) data with the aid of a motion estimator. To select and isolate malicious satellite signals and guarantee the minimum number of GPS signals for the localization, an enhanced fault detection and isolation (FDI) algorithm with a short-term memory has been developed in this research. When there are sufficient satellite signals for positioning, the horizontal dilution of precision (HDOP) has been applied for selecting the best four satellite signals to localize the mobile robot. Then, the GPS data are fused with INS data by a Kalman filter (KF) for a straight path and a curved motion estimator (CME) for a curved path. That is, the INS data are properly fused to the GPS data through the KF or CME process. To verify the effectiveness of the proposed algorithm, experiments using a mobile robot have been carried out on a university campus.

In this paper, a dynamic model of a seven-joints manipulator operated in a zero-g simulation system is established. The errors of the friction, the suspension force, and the flexible deformation of arms are considered. Furthermore, the unloading ratio, which can evaluate the performance of the simulation system, is presented. It can reflect the level of similarity between the system and the space environment directly and effectively. The results of experimental and theoretical analyses verify the correctness of the model. It helps us to get the joint torques when the actual space manipulator without the torque sensor operates in this system and guarantees the safety of the experiments.

The present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.

In this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.

This study presents an optimal design procedure including topology optimization and size–shape optimization methods to maximize mechanical advantage (which is defined as the ratio of output force to input force) of the synthesized compliant mechanism. The formulation of the topology optimization method to design compliant mechanisms with multiple output ports is presented. The topology-optimized result is used as the initial design domain for subsequent size–shape optimization process. The proposed optimal design procedure is used to synthesize an adaptive compliant gripper with high mechanical advantage. The proposed gripper is a monolithic two-finger design and is prototyped using silicon rubber. Experimental studies including mechanical advantage test, object grasping test, and payload test are carried out to evaluate the design. The results show that the proposed adaptive complaint gripper assembly can effectively grasp irregular objects up to 2.7 kg.

Parallel manipulators, especially those with outputs as one translation and two rotations (1T2R), are being increasingly studied. The kinematic chains of parallel manipulators share the loads and make the stiffness higher than the stiffness of serial manipulators with equivalent limbs. This high stiffness ensures a minimal deformation of the limbs, allowing a high positioning accuracy of the endeffector. Thus, it is very important to be able to measure the stiffness in parallel manipulators. In this work, we present a novel 1T2R multi-axial shaking table (MAST) for automobile pieces testing purposes—the 2PRU–1PRS parallel manipulator—and focus on the analysis of its stiffness all over the useful workspace. Analysis methods based on matrix structural method need to be validated for every parallel manipulator, and we present these steps along with a comparison between experimental and analytical methods.

Determining an appropriate grasp configuration for multi-finger grasping is difficult due to the complexity of robotic hands. The multi-finger grasp planning should consider not only geometry constraints of objects but also kinematics and dynamics of robotic hand. In this paper, a precision grasp-planning framework is presented for multi-finger hand to grasp unknown objects. First, the manipulation capabilities of the robotic hand are analyzed. The analysis results are further used as bases for the precision grasp planning. Second, the superquadric (SQ) fitting method is used for approximating unknown object models. Finally, a local–global optimization method is implemented to find appropriate grasp configurations for dexterous hand. The presented planning framework is validated in simulation experiments. Simulation results demonstrated that the presented grasp-planning framework enables the multi-finger hand to grasp unknown objects effectively.

This paper proposes a dynamic window with virtual goal (DW-VG) method for local collision avoidance in dynamic environments. Firstly, the debounce filter and polynomial curve-fitting algorithm are combined to predict the trajectory of the obstacles with timestamps. Based on the motion prediction of the obstacles, the virtual goal is proposed to replace the real goal, so that the robot can escape from the concave trap and avoid the dynamic obstacles. According to the timestamps and virtual goal, the optimal linear and angular velocities are selected from the dynamic window, which drive the robot toward its real goal. The simulation and experimental results show that the DW-VG method can not only escape the local minima and avoid dynamic obstacles but also is applicable to the dense environment. Furthermore, the simulation results also verify that the DW-VG method drives the robot to reach its goal faster and safer than other reactive obstacle avoidance methods.

Humans are experts in manipulation and grasping tasks. However, several industrial tasks represent a risk to human operators, for instance, handling radioactive material or transporting heavy objects. Teleoperation robotic schemes extend human capabilities, but they are highly nonlinear systems. In this paper, we address the problem of dexterous remote manipulation by means of a unilateral heterogenous teleoperation scheme composed by a single-master and multiple-slave manipulators handling a rigid object. In order to achieve a stable grasp, a decentralized force/position controller with continuous and bounded input torques based on the Orthogonalization Principle and a second-order sliding mode control is proposed for the slave robots. In addition, a trajectory planning method based on holonomic constraints is proposed to control multiple-slave manipulators with a single-master device. Experimental results are presented to evaluate the performance of the proposed approach.