The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

In this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.

Fossil fuel sources are well suited to fulfill the energy needs of human beings. Unfortunately, there are some limitations and disadvantages pertaining to fossil fuels, some of which are drastic. The main issues are: firstly, there is a finite supply of these fuels, eventually this supply will be exhausted; secondly, burning fossil fuels contributes to global warming, leading to disastrous consequences for the environment and the health of humans. Switching to renewable energy sources is the viable solution to the aforementioned issues. Robots bring numerous benefits in a wide variety of applications. Introducing robots to production environments and other applications results in a remarkable improvement in terms of productivity and efficiency. In this paper, the integration between robots and renewable energy sources is discussed. In other words, two main points are investigated: (1) how can renewable energy be a viable source of energy for robots and (2) how can the renewable energy industry benefit from utilizing robots in the execution of renewable energy-related tasks. Some of the recent developments concerning the integration between robots and renewable energy are reviewed. In addition, more opportunities and expected advancements are also discussed.

The consensus of higher-order nonlinear heterogeneous multiagent systems with matched and unmatched uncertainties is studied in this paper. The distributed observer-based controllers for multiagent systems are achieved using a fixed-time sliding mode controller based on the disturbance observer. For this purpose, the disturbance observers are designed for finite-time estimation of matched and unmatched uncertainties. Using the estimated values, the fixed-time distributed sliding mode controllers are designed and the consensus protocol is achieved. In this regard, a theorem is proved, which guarantees the fixed-time convergence of consensus errors. The effectiveness of the proposed distributed controllers has been validated through simulations for two robotic multiagent systems and a numerical example.

Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.

This paper proposes an adaptive robust impedance control for a single-link flexible arm when it encounters an environment at an unknown intermediate point. First, the intermediate collision point is estimated using a collision detection algorithm. The controller, then, switches from free to constrained motion mode. In the unconstrained motion mode, the exerted force to environment is nearly zero. Thus, the reference trajectory is a prescribed desired trajectory in position control. In the constrained motion mode, the reference trajectory is determined by the desired target dynamic impedance. The simulation results demonstrate the efficiency of proposed control scheme.

In human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions.

This paper describes a modeling approach to compute the lumped parameter hydrodynamic derivative matrices of an underwater multi-hull vehicle. The vehicle, modeled as a multi-body underwater system and denoted as cluster, can be composed by heterogeneous bodies with known dynamic parameters, rigidly connected. The nonlinear dynamic equations of the cluster and its parameters are derived by means of a modular approach, based on the composition of single basic elements. The ultimate objective is to derive a mathematical description of the multi-hull system that captures its most significant dynamics allowing to design model-based motion controllers and navigation filters. The modular nature of the resulting model can be exploited, by example, when control reconfiguration is to be dealt with in the presence of (possibly multiple) failures. The numerical simulation of a hypothetical cluster is presented and discussed.

This paper deals with a continuous design task of a planar cable robot used in a gait training machine called the cable-driven legs trainer. The design of cable robots requires satisfying two constraints, that is, tensions in the cables must remain non-negative, and cable interferences should be avoided. The carried design approach is based on interval analysis, which is one of the most efficient methods to obtain certified results. The constraints of non-negative tensions and cable to end-effector interference are solved using interval analysis tools. By means of a dynamic simulation, the reached workspace and the produced wrenches of the cable robot are evaluated as a set of interval vectors. An optimization algorithm is then designed to optimize the cable robot structure for the gait training machine. The robot is designed to produce non-negative tensions in the cables and to avoid collision at all times within the desired workspace and under the required external loads.