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 work addresses a new framework that proposes a decentralized strategy for collective and collaborative behaviours of multi-agent systems. This framework includes a new clustering behaviour that causes agents in the swarm to agree on attending a group and allocating a leader for each group, in a decentralized and local manner. The leader of each group employs a vision-based goal detection algorithm to find and acquire the goal in a cluttered environment. As soon as the leader starts moving, each member is enabled to move in the same direction by staying coordinated with the leader and maintaining the desired formation pattern. In addition, an exploration algorithm is designed and integrated into the framework so as to allow each group to be able to explore goals in a collaborative and efficient manner. A series of comprehensive experiments are conducted in order to verify the overall performance of the proposed framework.

In this paper, the feedback linearization approach is used to introduce a motion controller for unicycle-type wheeled mobile robots (UWMRs). The output function is defined as a linear combination of the error state. The novel scheme is firstly tested in numerical simulation and compared with its corresponding experimental result. Three controllers are taken from the literature and compared to the proposed approach by means of experiments. The gains of the experimentally tested controllers are selected to obtain identical energy consumption. The Optitrack commercial vision system and Pioneer P3-DX UWMR are used in real-time experimental tests. In addition, two sets of experimental results for different motion tasks are provided. The results show that the proposed controller presents the best tracking accuracy.

This paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in the SE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.

In this paper, a novel robust model reference adaptive impedance control (RMRAIC) scheme is presented for an active transtibial ankle prosthesis. The controller makes the closed loop dynamics of the prosthesis similar to a reference impedance model and provides asymptotic tracking of the response trajectory of this impedance model. The interactions between human and prosthesis are taken into account by designing a second-order reference impedance model. The proposed controller is robust against parametric uncertainties in the nonlinear dynamic model of the prosthesis. Also, the controller has robustness against bounded uncertainties due to unavailable ground reaction forces and unmeasurable feedbacks of accelerations at the socket place. Moreover, an appropriate Series Elastic Actuator (SEA) mechanism for the prosthetic ankle is included in this work and its effects are discussed. Tracking performance and stability of the closed-loop system are proven via the Lyapunov stability analysis. Using simulations on an overall amputee prosthetic foot system, the effectiveness of the proposed RMRAIC controller is investigated for the task of level ground walking.

This paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.

This paper presents an optimal trajectory planning method for industrial robots. The paper specially focuses on the applications of path tracking. The problem is to plan the trajectory with a specified geometric path, while allowing the position and orientation of the path to be arbitrarily selected within the specific ranges. The special contributions of the paper include (1) an optimal path tracking formulation focusing on the least time and energy consumption without violating the kinematic constraints, (2) a special mechanism to discretize a prescribed path integration for segment interpolation to fulfill the optimization requirements of a task with its constraints, (3) a novel genetic algorithm (GA) optimization approach that transforms a target path to be tracked as a curve with optimal translation and orientation with respect to the world Cartesian coordinate frame, (4) an integration of the interval analysis, piecewise planning and GA algorithm to overcome the challenges for solving the special trajectory planning and path tracking optimization problem. Simulation study shows that it is an insufficient condition to define a trajectory just based on the consideration that each point on the trajectory should be reachable. Simulation results also demonstrate that the optimal trajectory for a path tracking problem can be obtained effectively and efficiently using the proposed method. The proposed method has the properties of broad adaptability, high feasibility and capability to achieve global optimization.

This work presents a novel trajectory tracking, hovering, and yaw motion control for quadrotors subject to unknown modeling uncertainties and disturbances. Nonlinear equations of motion are used to model the quadrotor's motion without any simplifying assumptions. An integral backstepping control is developed by defining the tracking errors, their integral, and their first through third time derivatives as the system states. The resulting surge force and roll and pitch moments are shown to asymptotically stabilize the error states subject to bounded disturbances and modeling uncertainties. Similarly, a yaw moment is derived through integral backstepping that simultaneously stabilizes yaw motion errors. The controller performance in simultaneous trajectory and yaw motion tracking is verified through both simulations and experiments.

To improve biped locomotion’s robustness to internal and external disturbances, this study proposes a hierarchical structure with three control levels. At the high level, a foothold sequence is generated so that the Center of Mass (CoM) trajectory tracks a planned path. The planning procedure is simplified by selecting the midpoint between two consecutive Center of Pressure (CoP) points as the feature point. At the middle level, a novel robust hybrid controller is devised to drive perturbed system states back to the nominal trajectory within finite cycles without chattering. The novelty lies in that the hybrid controller is not subject to linear CoM dynamic constraints. The hybrid controller consists of two sub-controllers: an oscillation controller and a smoothing controller. For the oscillation controller, the desired CoM height is specified as a sine-shaped function, avoiding a new attractive limit cycle. However, this controller results in the inevitable chattering because of discontinuities. A smoothing controller provides continuous properties and thus can inhibit the chattering problem, but has a smaller region of attraction compared with the oscillation controller. A hybrid controller merges the two controllers for a smooth transition. At the low level, the desired CoM motion is defined as tasks and embedded in a whole body operational space (WBOS) controller to compute the joint torques analytically. The novelty of the low-level controller lies in that within the WBOS framework, CoM motion is not subject to fixed CoM dynamics and thus can be generalized.

Tensegrity mechanisms are composed of rigid and tensile parts that are in equilibrium. They are interesting alternative designs for some applications, such as modeling musculo-skeleton systems. Tensegrity mechanisms are more difficult to analyze than classical mechanisms as the static equilibrium conditions that must be satisfied generally result in complex equations. A class of planar one-degree-of-freedom tensegrity mechanisms with three linear springs is analyzed in detail for the sake of systematic solution classifications. The kinetostatic equations are derived and solved under several loading and geometric conditions. It is shown that these mechanisms exhibit up to six equilibrium configurations, of which one or two are stable, depending on the geometric and loading conditions. Discriminant varieties and cylindrical algebraic decomposition combined with Groebner base elimination are used to classify solutions as a function of the geometric, loading, and actuator input parameters.

This paper presents a lower-limb exoskeleton that is actuated by pneumatic muscle actuators (PMAs). This exoskeleton system is composed of the mechanical structures, a treadmill, and a weight support system. With the cooperative work of the three parts, the system aims to assist either the elderly for muscle strengthening by conducting walking activities or the stroke patients during a rehabilitation training program. A mechanism is developed to separate the PMAs from the wearer’s legs to reduce the subject’s physical exertion. Furthermore, considering the difficulty in the modeling of proposed PMAs-driven exoskeleton, a safe and model-free control strategy called proxy-based sliding mode control (PSMC) is used to ensure proper control of the exoskeleton. However, the favorable performances are strongly dependent on the appropriate control parameters, which may be difficult to obtain with blind tuning. Therefore, we propose a global parameters optimization algorithm called switch-mode firefly algorithm (SMFA) to automatically calculate the pre-defined object function and attain the most applicable parameters. Experimental studies are conducted, and the results show the effectiveness of the proposed method.

Ditch crossing is one of the essential capabilities required for a biped robot in disaster management and search and rescue operations. Present work focuses on crossing a wide ditch with landing uncertainties by an under-actuated planar biped robot with five degrees of freedom. We consider a ditch as wide for a robot when the ankle to ankle stretch required to cross it is at least equal to the leg length of the robot. Since locomotion in uncertain environments requires real-time planning, in this paper, we present a new approach for generating real-time joint trajectories using control constraints not explicitly dependent on time, considering impact, dynamic balance, and friction. As part of the approach, we introduce a novel concept called the point of feasibility for bringing the biped robot to complete rest at the end of ditch crossing. We present a study on the influence of initial posture on landing impact and net energy consumption. Through simulations, we found the best initial postures to efficiently cross a wide ditch of width 1.05 m, with less impact and without singularities. Finally, we demonstrate the advantage of the proposed approach to cross a wide ditch when the surface friction is not same on both sides of the ditch.

In this paper, we propose a novel mechanism to compensate for gravity and the gripping force in a hand-arm robot. This mechanism compensates for the gravitational torque produced by an object gripped by the hand-arm robot. The gripping force required for the robot hand to prevent the object from dropping is also simultaneously compensated for. This mechanism requires only one actuator placed on the shoulder part of the robot. Therefore, this mechanism can reduce the torque requirement of joint actuators and lower the weight of the robot. The gear ratio of the reduction gears in each robot joint can then also be reduced. These advantages are critical for future robots that perform tasks in unstructured environments and collaborate with humans. We carried out experiments with a 6-DoF robot arm having a 1-DoF gripper to demonstrate the effectiveness of the proposed mechanism.

In this paper, a neural network (NN)-based tracking controller is proposed for a general class of type (m,s) wheeled mobile manipulators (WMMs) subjected to model uncertainties with prescribed transient and steady-state performance specifications. First, an input–output model of WMMs is derived by introducing proper output equations. Then, the prescribed performance technique is employed to propose a proportional integral derivative trajectory tracking controller for WMMs to ensure that the tracking errors converge to a smaller, arbitrary ultimate bound with a predefined maximum overshoot/undershoot and convergence speed. The learning capabilities of multilayer NNs are incorporated into the controller to approximate the uncertain nonlinear dynamics of the robot. An adaptive saturation-type controller is utilized to compensate NN estimation errors and external disturbances. A Lyapunov-based stability analysis is used to demonstrate that the tracking errors are uniformly ultimately bounded and converge to a small neighborhood of zero with a guaranteed prescribed performance. Numerical computer simulations are presented to show the effectiveness of the proposed controller.

This paper presents the design, analysis, and experimental validation of a miniature modular inchworm robot (MMIR). Inchworm robots are capable of maneuvering in confined spaces due to their small size, a desirable characteristic for surveillance, exploration and search and rescue operations. This paper presents two generations of the MMIR (Version 1—V1 and Version 2—V2) that utilize anisotropic friction skin and an undulatory rectilinear gait to produce locomotion. This paper highlights design improvements and a multi-body dynamics approach to model and simulate the system. The MMIR V2 incorporates a slider-crank four-bar mechanism and a relative body revolute joint to produce high-frequency relative translation and rotation to increase forward velocity and enable turning capabilities. Friction analysis and locomotion experiments were conducted to assess the systems performance on various surfaces, validate the dynamic model and simulation results, and measure the maximum forward velocity. The MMIR V1 and V2 were able to achieve maximum forward velocities of 12.7 mm/s and 137.9 mm/s, respectively. These results are compared to reported results of similar robots published in the literature.

In this paper, a parametric analysis of the inverse dynamics of an upright partially unloaded walking is performed. This motion is produced through a gait-training machine emulating the over-ground walking using a body weight support mechanism and a cable-driven robot. The input motion is the kinematics of a normal gait, and the ultimate output result is the required tensions to be generated by the cable robot in order to drive the lower limb. The dynamic analysis is carried out based on the Newton–Euler approach. A Matlab Simscape model is also built to validate the analytical results. The obtained dynamic model is used to investigate the effect of the variation of the gait simulation parameters on the actuation wrench and the cable tensions. The obtained results could be used to determine the optimal design of the gait trainer actuators and they are useful in estimating optimal gait training parameters.

Cooperative manipulators have uncertainties in their structure; therefore, an optimal sliding mode control method is derived from a combination of the sliding mode control (SMC) and the state-dependent Riccati equation (SDRE) technique. This proposed combination is applied to a class of non-linear closed-loop systems. One of the distinguished features of this control method is its robustness toward uncertainty. Due to the lack of optimality in the SMC method, in this paper, a robust and optimal method is presented by considering the SDRE in design of the sliding surface. Due to the fact that cooperative manipulators have been used for carrying loads, the percentage of load distributions between each manipulator has been derived to increase the dynamic load carrying capacity (DLCC). The proposed control structure is implemented on a Scout robot with two manipulators in cooperative mode, theoretically and practically using LabVIEW software; and the results were compared by considering the uncertainty in its structure. In comparison with the SDRE, the proposed method increased the DLCC almost 10% in the Scout case.

Nonlinear articular geometries of biological joints have contributed to highly agile and adaptable human-body motions. However, human–machine interaction could potentially distort natural human motions if the artificial mechanisms overload the articular surfaces and constrain biological joint kinematics. It is desired to better understand the deformable articular geometries of biological joints in vivo during movements for design and control of wearable robotics. An articular geometry reconstruction method is proposed to measure the effective articular profile with a wearable compliant device and illustrated with its application to knee-joint kinematic analysis. Regarding the joint articulation as boundary constraints for the compliant mechanism, the equivalent articular geometry is constructed from the beam deformations driven by knee motions, where the continuous deformations are estimated with strain data from the embedded sensors. Both simulated analysis and experimental validation are presented to justify the proposed method.

In this paper, a type of parallel robot with three translational degrees of freedom is studied. Inverse and forward kinematic equations are extracted for position and velocity analyses. The dynamic model is derived by Lagrange’s approach and the principle of virtual work and related computational algorithms implementing inverse and forward dynamics are presented. Furthermore, some numerical simulations are performed using the kinematic and dynamic models in which the results show good agreement with expected qualitative behavior of the mechanism. Comparisons with the results of work-energy and impulse-momentum methods quantitatively verify the validity of the derived equations of motion. Also, a relative computational effectiveness is observed in implementation of virtual work model via the simulations.

This document proposes a control scheme for delayed bilateral teleoperation of a mobile robot, which it is sought to achieve a coordination of the master device position with the slave mobile robot velocity, and at the same time synchronize the force exerted by the operator with force applied by the environment over the mobile robot. This approach allows the operator to improve the sensitive perception of the remote environment in which the robot navigates while he generates commands to control the mobile robot motion. In this paper, variable and asymmetrical communication time delays are taken into account, as well as a non-passive model of the human operator, for which a novel model is proposed that has a more general structure than the typical ones used to date in the teleoperation field. Furthermore, based on the theoretical analysis presented, the state of convergence in the stationary response is obtained. In addition, an experimental performance evaluation is carried out, where the position–velocity error, force error and the time to complete the task are evaluated. In the tests, a human operator commands a remote mobile robot to push objects of different weight while he perceives the weight of each object through the force feedback system. As an outcome, the theoretical and practical results obtained allow concluding that a satisfactory trade-off between stability and transparency is reached.