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Current implementations of leader-follower systems in multi-robot environments typically separate path planning and formation control, often leading to inefficiencies in execution speed, path quality, and system stability. These systems rely on complex control strategies that struggle to adapt to dynamic and challenging environments, resulting in sub-optimal performance and potential collisions. This paper presents a novel approach to enhancing the path planning and formation control of multi-robot leader-follower systems by integrating neural fields and novel potential field modeling. The primary focus is on developing a unified platform that addresses the challenges of maintaining formation integrity, optimizing path quality, and ensuring real-time responsiveness. Neural Fields, which has gained significant traction in the past few years, utilizes fully connected neural networks to effectively encode continuous signals across various dimensions and resolutions. This growing interest in neural fields has highlighted their potential to provide solutions that are not only more precise and high-fidelity but also highly expressive and efficient in terms of memory usage. Thus, this work proposes leveraging neural fields to achieve high-quality path planning in significantly reduced time. By utilizing the expressiveness and memory efficiency of neural fields, the suggested approach aims to generate optimized paths quickly, making it well-suited for real-time applications where both accuracy and speed are critical. On the other hand, the follower robots are equipped with an auto-switching potential method, which intelligently toggles between attractive forces guiding the followers toward the leader and repulsive forces preventing collisions based on a mathematical model. The effectiveness of this approach is validated through experiments, demonstrating significant improvements in execution speed, path smoothness, and overall system stability compared to competitive methods, including well-known techniques such as A*, Probabilistic RoadMap (PRM), Rapidly-exploring Random Tree Star (RRT*), and also against recent optimization techniques including Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA).

In critical manoeuvres where the maximum tire-road friction capacity is used, the vehicle's dynamic behaviour is highly nonlinear, and there are strong couplings between longitudinal and lateral dynamics. If the tire-road friction conditions change suddenly during these manoeuvres, the vehicle control will be very complicated. The innovation of this research is a control algorithm to manage vehicles on a curved path with sudden tire-road friction change. The main advantage of the proposed controller is that it is robust to the change of the friction coefficient and other unmodeled uncertainties and ensures vehicle stability with low computational volume. The evaluation of the proposed adaptive controller has been done using the full vehicle model in CarSim software and by defining three different manoeuvres, moving at a constant speed on a curved road, lane-change, and lane-change with braking. Also, in the obtained results, the noise of the yaw speed signals and longitudinal and lateral accelerations are considered. The estimation of the longitudinal and lateral velocities is also done using these data. The obtained results showed that the proposed integrated control can manage the highly nonlinear dynamics of the vehicle in the existence of a sudden and significant change in the friction coefficient.

In many wheeled robot applications, in addition to accurate position control, dimensional and weight limitations are also important. The limitation of weight and dimensions means that it is not possible to use arbitrarily large actuators. On the other hand, accurate and fast tracking usually requires high control gains and, as a result, large control inputs. If the control input exceeds the saturation limit of the operator, in addition to increasing the tracking error, it may lead to robot instability in some cases. Therefore, it will be precious to provide a control method that can simultaneously provide high control accuracy and guarantee the robot's stability, taking into account the saturation limit of the actuators (speed and torque) in a predetermined manner. This issue has been addressed in the present study. The proposed control includes two parts: a kinematic controller and a dynamic controller. The kinematic control design is based on the Lyapunov approach, which can adjust the speed saturation limit of the actuators. For dynamic control, the robot velocity components are considered as control reference values and the robot wheel torque is considered as control inputs. In the dynamic control design, the torque saturation limit of the actuators is included in a predetermined way. To evaluate the performance of the proposed nonlinear control, various analyses were performed on the wheeled robot. The results showed that the proposed control algorithm while guaranteeing stability and following the path with high accuracy, has also fully met the requirements of the actuators’ saturation limits. [ABSTRACT FROM AUTHOR]

In critical manoeuvres where the maximum tire-road friction capacity is used, the vehicle's dynamic behaviour is highly nonlinear, and there are strong couplings between longitudinal and lateral dynamics. If the tire-road friction conditions change suddenly during these manoeuvres, the vehicle control will be very complicated. The innovation of this research is a control algorithm to manage vehicles on a curved path with sudden tire-road friction change. The main advantage of the proposed controller is that it is robust to the change of the friction coefficient and other unmodeled uncertainties and ensures vehicle stability with low computational volume. The evaluation of the proposed adaptive controller has been done using the full vehicle model in CarSim software and by defining three different manoeuvres, moving at a constant speed on a curved road, lane-change, and lane-change with braking. Also, in the obtained results, the noise of the yaw speed signals and longitudinal and lateral accelerations are considered. The estimation of the longitudinal and lateral velocities is also done using these data. The obtained results showed that the proposed integrated control can manage the highly nonlinear dynamics of the vehicle in the existence of a sudden and significant change in the friction coefficient. [ABSTRACT FROM AUTHOR]

Traditional kinematic control of mobile robots primarily regulates position and orientation, often neglecting dynamic factors such as acceleration and torque, making it suitable for low-speed operations. However, to enhance safety and efficiency, it is crucial to consider dynamic constraints in the robot’s control system, including maximum velocity and angular velocity. Existing kinematic control methods typically fail to incorporate these dynamic limitations. This paper proposes a novel method for optimizing control gains for mobile robots, factoring in dynamic constraints. We introduce optimal control concepts to kinematic and dynamic controllers, employing a genetic algorithm to identify optimal control gains. Furthermore, we leverage a neural network with robust interpolation capabilities to select control gains for arbitrary initial poses effectively. The trained neural network accurately predicts control gains across various initial conditions, as simulation results confirm. The performance of the proposed neural network controller for diverse mobile robot postures is nearly equivalent to that of a controller using optimization gains derived from a genetic algorithm. In experiments with various robot postures, the maximum performance error time recorded was 0.44 seconds, reflecting a delay of 3.2% in arrival time. This approach enables mobile robots to reach target destinations with improved stability and performance, addressing the limitations inherent in traditional kinematic control methods.

This work presents a novel method for 3-D path-following and path-tracking of Autonomous Underwater Vehicles (AUVs) using the concept of a Virtual Reference Point (VRP) and a kinematic guidance principle. The origins of the along-, cross- and vertical-track errors are proven globally exponentially stable (GES) using Lyapunov stability analysis. The kinematic guidance law exploits the design flexibility of a user-defined VRP in conjunction with a feedback linearizing controller. In addition, a novel concept called the Handy Matrix is introduced and applied to shape the kinematic equations such that the AUV’s non-actuated degrees of freedom (DOFs) can be controlled in a 3-D path-following scenario. The case study considers the Remus 100, a torpedo-shaped underactuated AUV, performing a 3-D path-following maneuver. The computer simulations show that the kinematic guidance law shows excellent tracking performance and stability even in the presence of ocean currents and white measurement noise.

Kinematic control of redundant serial manipulators has been carried out for the past half century based primarily on a generalized inverse velocity formulation that is known to have mathematical deficiencies. A recently developed inverse kinematic configuration mapping is employed in an operational configuration space differentiable manifold formulation for redundant-manipulator kinematic control with obstacle avoidance. This formulation is shown to resolve deficiencies in the generalized inverse velocity formulation, especially for high-degree-of-redundancy manipulators. Tracking a specified output trajectory while avoiding obstacles for four- and twenty-degree-of-redundancy manipulators is carried out to demonstrate the effectiveness of the differentiable manifold approach for applications with a high degree of redundancy and to show that it indeed resolves deficiencies of the conventional generalized inverse velocity formulation in challenging applications. [ABSTRACT FROM AUTHOR]

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The objective of this work is to devise an effective control system for addressing the trajectory tracking challenge in nonholonomic mobile robots. Two primary control approaches, namely kinematic and dynamic strategies, are explored to achieve this goal. In the kinematic control domain, a backstepping controller (BSC) is introduced as the core element of the control system. The BSC is utilized to guide the mobile robot along the desired trajectory, leveraging the robot's kinematic model. To address the limitations of the kinematic control approach, a dynamic control strategy is proposed, incorporating the dynamic parameters of the robot. This dynamic control ensures real-time control of the mobile robot. To ensure the stability of the control system, the Lyapunov stability theory is employed, providing a rigorous framework for analyzing and proving stability. Additionally, to optimize the performance of the control system, a genetic algorithm is employed to design an optimal control law. The effectiveness of the developed control approach is demonstrated through simulation results. These results showcase the enhanced performance and efficiency achieved by the proposed control strategies. Overall, this study presents a comprehensive and robust approach for trajectory tracking in nonholonomic mobile robots, combining kinematic and dynamic control strategies while ensuring stability and performance optimization. [ABSTRACT FROM AUTHOR]

The leader-follower formation approach is a commonly used strategy in multi-robot systems, usually implemented with a hierarchical control architecture combining path planning and formation control. The leader robot determines the desired trajectory while the follower robots track the motion of the leader robot using a control system. However, this hierarchical architecture does not ensure successful obstacle avoidance for follower robots. Several solutions proposed adding an obstacle avoidance layer, but this can increase the system complexity and reduce the computational speed, hindering real-time performance. Improving the opposing attributes, namely the execution speed, path length, safety, and smoothness, together is a challenging path-planning problem in robotics. This paper proposes a novel leader-follower control mechanism that combines formation control and obstacle avoidance in one step. The new path planning technique focuses on enhancing execution speed and safety while ensuring the generation of smooth paths with acceptable path lengths. The main contribution of the proposed technique lies in the development of a novel potential field modeling approach specifically designed for follower robots in a multi-robot system. The proposed potential field model consists of three terms, namely, the Gaussian term, the Euclidean term, and the Logarithmic term, which are all optimized later using Particle Swarm Optimization (PSO) to generate the path. The Gaussian term, acting as a repulsive force, represents the Gaussian distance to each obstacle in the environment. It exhibits a strong value in close proximity to obstacles, while it gradually decays exponentially as the distance from the obstacles increases. The second term, the Euclidean term, which is the Euclidean distance to the leader robot, is responsible to find the shortest path to the leader robot. Finally, to ensure follower robot safety, a logarithmic term is integrated into the potential field model, facilitating automatic switching between attractive and repulsive forces generated by the leader robot. The incorporation of a logarithmic term into the potential field model stands as a significant innovation in the proposed technique. This inclusion enables the leader robot to generate an initial attractive force towards the followers, which dynamically transitions into a repulsive force as the follower robots approach. This automatic switching behavior enhances processing efficiency while ensuring collision avoidance. A kinematic control strategy is applied to the system in order to test the proposed path planning technique. The experimental results have proven the effectiveness of the proposed system, which has shown superior performance over the well-known techniques A*, RRT*, PRM, and also Hybrid-A* in terms of execution speed and the path length.

This paper proposes three new dynamic model-based adaptive sliding mode controllers (ASMC) for trajectory tracking control of unmanned ground vehicles (UGV). The controllers combine kinematic and dynamic control to achieve asymptotic stability and finite-time convergence and their stability is verified using the Lyapunov stability theory. The controllers are designed to address the challenges posed by uncertainties in vehicle dynamic parameters and outperform the sliding mode control (SMC) method in trajectory tracking control. The simulation results of the proposed controllers are also discussed in terms of performance metrics. The simulation results show that the controller proposed in rule 1 shows the better performance in terms of trajectory tracking, and then the controllers proposed in rules 2 and 3 have good performances, respectively. In addition, when the simulation results are compared, it is seen that the performance of all three proposed controllers is better than the SMC.

The traditional underwater manipulator has large volume, complex structure and limited space, and its performance in underwater environment is poor. In order to complete underwater operations under the conditions of narrow space, dangerous environment and complex tasks, a cabledriven snake-like manipulator (CDSLM) was designed for complicated underwater scenarios. Firstly, driven by a gear-rack structure, a servomotor was used to control two transmission cables simultaneously, and thus the number of the motors and energy consumption of cable-driven snake-like manipulator were reduced. Apart from it, the multi-level mapping between the motors, cables, joints and the end-effector was analyzed, and the corresponding kinematics equations were deduced and solved. Finally, according to the multi- level mapping relationship between cable-driven snake-like manipulators, a control strategy based on fuzzy controller was designed to reduce the tracking error caused by the transmission system and realize the precise control of the pose of the snake-like manipulator, which solves the problem that it was difficult to obtain an accurate dynamic model of the cable-driven snake-like manipulator. The MATLAB was used to analyze the tracking performance of cable-driven snake-like manipulator. The results show that the yaw angle maximum tracking error of cable-driven snake-like manipulator does not exceed 0.05 mm, the pitch angle maximum tracking error is less than 0.09 mm, which verifies the effectiveness of the kinematics control strategy based on fuzzy controller. [ABSTRACT FROM AUTHOR]

Displacements of the base during trajectory tracking are a common issue in the control of aerial manipulators. These are caused by reaction torques transferred to the base due to the manipulator motion and, in particular, to the motion of its center of gravity. We present a novel approach to reduce base displacements of a kinematically redundant aerial manipulator by using null-space projection in the inverse kinematic control. A secondary objective function minimizes the horizontal displacement of the manipulator center of gravity. We test this algorithm on different trajectories for both three and four degrees of freedom (DOF) manipulators in a simulation environment. The results comparing our algorithm with inverse kinematic control without the null-space projection show up to an 80% reduction in the end-effector position error and an average of about 56% reduction in maximum base displacement. The simulation implementation also runs faster than in real-time in our code implementation. We provide a workspace analysis based on multiple stopping criteria such as excessive base displacement, joint velocities and end-effector position error for the 3 and 4 DOF manipulators. As expected, the 4 DOF manipulator has a larger workspace. [ABSTRACT FROM AUTHOR]

The primal-dual gradient dynamics is a broadly investigated approach for handling optimisation problems. In this paper, we provide an extension of such dynamics under the adaptive updating framework for solving equality-constrained quadratic programmes. We show that the performance of the proposed method is theoretically guaranteed and it has asymptotic convergence to the solution of the optimisation problem and the minimum inter-event time is non-trivial. A numerical example and an application show the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The aim of this paper is the development of a redundancy resolution scheme for manipulators able to cope with kinematic constraints. In detail, the structure of the controller is of weighted least norm (WLN) type. The constraints are modeled as unilateral inequalities and can be general scalar functions (linear or nonlinear) of both the joint position and the joint velocity variables. In this work, a general procedure is proposed in order to include constraints of different types, namely functions of joint position or velocity only, functions of both joint position and velocity with a time dependent or time independent threshold. Simulations are performed in Matlab-Simulink environment and two tests are performed: the first employs a single 7-DOF arm, while in the second a dual-arm system composed of two 7-DOF manipulators is used. Results show that the proposed redundancy resolution scheme is capable of satisfying complex inequality constraints where other known methods fail. [ABSTRACT FROM AUTHOR]

Continuum manipulators are a type of robot used for delicate applications, including safe human-robot interactions. Controlling these manipulators for an accurate trajectory, especially in the case of pneumatic actuation, is extremely challenging. Thus, this article proposes a real-time kinematic trajectory control of a pneumatically actuated multi-segment bionic continuum manipulator with a mobile base by combining a neural network and analytical model with a cascaded controller to overcome this challenge. The inverse kinematics solution of the multi-segment manipulator is developed by using a neural network and an inverse piecewise constant curvature approach. The neural network is trained by using a separate learning algorithm. Although hybrid inverse modeling gives better solutions than existing techniques, significant residual positional error of the manipulator tip remains due to inherent material hysteresis. Thus, a cascaded PI-controller is utilized to compensate for the residual positional error. The controller gains are updated in each step by predictions of the actuator length, where the Jacobian entries are computed from the neural network model. The proposed procedure is validated on Festo Didactics’ elephant trunk-like two-segment continuum manipulator, Robotino-XT. Three different cases are considered for real-time trajectory tracking, where the OptiTrack vision system is used for validation by tracking the manipulator tip pose. For the trajectory points outside the manipulator workspace, simultaneous trunk and base movements are used. In experimental validation, the proposed scheme is shown to give much reduced manipulator tip trajectory error as compared to the existing methods.

Kinematic control of redundant serial manipulators has been carried out for the past half century based primarily on a generalized inverse velocity formulation that is known to have mathematical deficiencies. A recently developed inverse kinematic configuration mapping is employed in an operational configuration space differentiable manifold formulation for redundant-manipulator kinematic control with obstacle avoidance. This formulation is shown to resolve deficiencies in the generalized inverse velocity formulation, especially for high-degree-of-redundancy manipulators. Tracking a specified output trajectory while avoiding obstacles for four- and twenty-degree-of-redundancy manipulators is carried out to demonstrate the effectiveness of the differentiable manifold approach for applications with a high degree of redundancy and to show that it indeed resolves deficiencies of the conventional generalized inverse velocity formulation in challenging applications.

With the rise in new soft robotic applications, the control requirements increase. Therefore, precise control methods for soft robots are required. However, the dynamic control of soft robots, which is required for fast movements, is still an open topic and will be discussed here. In this contribution, one kinematic and two dynamic control methods for soft robots are examined. Thereby, an LQI controller with gain scheduling, which is new to soft robotic applications, and an MPC controller are presented. The controllers are compared in a simulation regarding their accuracy and robustness. Additionally, the required implementation effort and computational effort is examined. For this purpose, the trajectory tracking control of a simple soft robot is studied for different trajectories. The soft robot is beam-shaped and tendon-actuated. It is modeled using the piecewise constant curvature model, which is one of the most popular modeling techniques in soft robotics. In this paper, it is shown that all three controllers are able to follow the examined trajectories. However, the dynamic controllers show much higher accuracy and robustness than the kinematic controller. Nevertheless, it should be noted that the implementation and computational effort for the dynamic controllers is significantly higher. Therefore, kinematic controllers should be used if movements are slow and small oscillations can be accepted, while dynamic controllers should be used for faster movements with higher accuracy or robustness requirements. [ABSTRACT FROM AUTHOR]

Featured Application: Inspection of structures, e.g., offshore/nuclear/eolic plants, bridges, and tall buildings. Placement and retrieval of sensors. Assembly of structures in places not accessible/safe for humans. Aerial manipulators are used in industrial and service robotics tasks such as assembly, inspection, and maintenance. One of the main challenges in aerial manipulation is related to the motion of the UAV base caused by manipulator disturbance torques and forces, which jeopardize the precision of the robot manipulator. In this paper, we propose two novel inverse kinematic control methods used to track a trajectory with an aerial manipulator while also considering resultant UAV base motions. The first method is adapted from the generalized Jacobian formulation used in space robotics and includes the change in system momentum resulting from gravity and UAV control forces in the inverse kinematic control equation. This approach is simulated for a 2 and 3 degree-of-freedom aerial manipulator tracking trajectories with the end-effector. Although the end-effector position error is approximately zero throughout the simulated task, we see significant undesired UAV base motions of several centimeters in magnitude. To ameliorate this by exploiting the kinematic redundancy, we modify the generalized Jacobian by adding an additional task constraint which minimizes the reaction torques from the manipulator, to form the extended generalized Jacobian. While the second approach results in improved precision and reduced base motion by an order of magnitude as compared to the generalized Jacobian, a drawback is the reduction in the available workspace as the solution seeks to minimize the manipulator center of gravity translation. We also demonstrate and compare both approaches in a load picking task. All the algorithms are completed computationally faster than real time in the MATLAB simulations, illustrating their potential for application in real-world experiments. [ABSTRACT FROM AUTHOR]

Time-optimal kinematic control is a vital concern for industrial manipulators to save allocated motion task time as much as possible. This requires maximizing the end-effector velocity to minimize the time required for path tracking. Nonetheless, it remains a challenge to ensure that joint motion constraints are not violated during this process, even with the aim of maximizing end-effector velocity simultaneously. This paper introduces a novel approach, which for the first time leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to attain time-optimal kinematic control for manipulators. The theoretical analysis of the RNN-based kinematic control solver is addressed, ensuring both its optimality and convergence for achieving time-optimal kinematic control. The proposed method enables the maximization of end-effector velocity to achieve time-optimal kinematic control without violating all joint velocity limits simultaneously. In contrast to previous kinematic control schemes, the proposed method can enhance the end-effector path tracking speed of completion by 100% around, we substantiate the effectiveness and superiority of the proposed approach via simulation and V-Rep experiment on the manipulators. • A recurrent neural network for time-optimal kinematic control is newly proposed. • Stable joint-velocity level optimal resolution is theoretically offered. • The method is superior in time optimality and compliant with velocity constraints. [ABSTRACT FROM AUTHOR]

Ensuring driving stability in wheeled mobile robots (WMRs) within dynamic environments is crucial for reliable navigation. This study presents the design and testing of a double-wishbone suspension (DWS), which is specifically tailored for a highly maneuverable six-WMR configuration, to address stability challenges in unstructured terrains. During the suspension design phase, critical factors such as the link length, position of shock absorber, spring and damping coefficients, and roll center location were optimized using the non-dominated sorting genetic algorithm (NSGA). The proposed DWS module ensures robust and stable driving performance for medium-sized WMRs. It effectively reduces rollovers and external shocks on uneven terrains while maintaining consistent traction across all wheels. Unlike current applications of the DWS in robotics, all the optimized parameters of the DWS with the NSGA algorithm are tailored for high-speed travel and are proficient at absorbing impacts that are encountered during outdoor driving. For practical implementation, a fabricated platform with optimal design parameters was subjected to field tests to evaluate its driving performance, both in prolonged driving on a circular route and in outdoor settings, with bumpy obstacles. The study presents a comprehensive stability analysis of the DWS and the proposed mobile robot, with a specific emphasis on rollover scenarios. The experimental results unequivocally demonstrated that the six-WMR equipped with the proposed DWS outperforms its counterpart without the DWS. This study highlights the reliability of the proposed DWS in the six-WMR configuration for efficient outdoor operations in unstructured terrains. [ABSTRACT FROM AUTHOR]

Redundant manipulators play a critical role in industry and academia, which can be controlled from the kinematic or dynamic perspective. The motion-force control of redundant manipulators is a core problem in robot control, especially for the task requiring keeping contact with objectives, such as cutting, polishing, deburring, etc. However, when a manipulator’s model structure is unknown, it is challenging to take motion-force control of redundant manipulators. This article proposes a data-driven-based motion-force control scheme, which solves the motion-force control problem from the kinematic perspective. The scheme can take effect and estimate the structure information, i.e., the model parameters involved in the forward kinematics when the structure of the manipulator is incomplete or unknown. A recurrent neural network is devised to find the solution to the scheme. Besides, the theoretical analysis is presented to prove the correctness of the scheme. Simulations and physical experiments running on seven degrees of freedom redundant manipulators illustrate the superb performance and practicability of the scheme intuitively. The key contribution of this article is that, for the first time, a motion-force control scheme aided with data-driven technology is proposed from a kinematic perspective for the redundant manipulators. [ABSTRACT FROM AUTHOR]

The traditional deep-water manipulators have several problems to work in confined spaces, such as large volume, complex structure, and inability. To solve these problems, a novel cable-driven snake-like manipulator robot for deep-water is proposed. In this study, the structure design of the cable-driven snake-like manipulator robot is first introduced. Then, we establish the kinematics model of the proposed cable-driven snake-like manipulator robot, which includes three parts: motor-cable kinematics, cable-joint kinematics, and joint-end kinematics. Especially, a tip-following algorithm (Supplemental Material) is presented to fit the confined and complicated underwater scenarios. Furthermore, a kinematics control strategy based on fuzzy PID controller is presented to reduce the tracking error caused by transmission mechanism, and the simulation of the cable-driven snake-like manipulator is carried out based on the MATLAB. The results demonstrate that the tracking error is less than 0.04 mm, which shows the proposed control strategy is effective. [ABSTRACT FROM AUTHOR]

In this paper, an efficient novel method for tracking the linear deformable objects (LDOs) in real time is proposed. The method is developed based on recursively slicing a pointcloud into smaller pointclouds with sufficiently small variance. The performance of this method is investigated through a series of experiments with various camera resolutions in simulation when a robot end effector tracking an LDO using an RGBD camera, and in real word when the camera tracks a rope during a swing. The performance of the proposed method is compared with another state-of-the-art technique and the outcome is reported here. [ABSTRACT FROM AUTHOR]

The paper proposes a new design of an artificial neural network for solving the inverse kinematics problem of the anthropomorphic manipulator of robot SAR-401. To build a neural network (NN), two sets were used as input data: generalized coordinates of the manipulator and elements of a homogeneous transformation matrix obtained by solving a direct kinematics problem based on the Denavi–Hartenberg notation. According to the simulation results, the NN based on the homogeneous transformation matrix showed the best accuracy. However, the accuracy was still insufficient. To increase the accuracy, a new NN design was proposed. It consists of adding a so-called "correctional" NN, the input of which is fed the same elements of the homogeneous transformation matrix and additionally the output of the first NN. The proposed design based on the correctional NN allowed the accuracy to increase two times. The application of the developed NN approach was carried out on a computer model of the manipulator in MATLAB, on the SAR-401 robot simulator, as well as on the robot itself. [ABSTRACT FROM AUTHOR]

For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control process. In order to improve sparsity of joint actuation for manipulator systems, a novel motion planning scheme which can optimally and sparsely adopt joint actuation is proposed in this brief. The proposed motion planning strategy is formulated as a constrained $L_{1}$ norm optimization problem, and an equivalent enhanced optimization solution dealing with bounded joint velocity is proposed as well. A new primal dual neural network with a new solution set division is further proposed and applied to solve such bounded optimization which can sparsely adopt joint actuation for motion control. Simulation and experiment results demonstrate the efficiency, accuracy and superiority of the proposed method for optimally and sparsely adopting joint actuation. The average sparsity (i.e., - $\|\dot \theta \|_{p}$ where $\theta $ denotes the joint angle) of the joint motion of the manipulator can be increased by 39.22% and 51.30% for path tracking tasks in $X - Y$ and $X - Z$ planes respectively, indicating that the sparsity of joint actuation can be enhanced. [ABSTRACT FROM AUTHOR]

Redundant manipulators are widely utilized in numerous applications among various areas in industry and service. Redundant manipulators take advantage of their inherent or acquired redundancy to achieve certain benefits in kinematic control. Different from non-redundant manipulators, optimization paradigms are more likely to be established and may be more efficient for kinematic control issues in redundant manipulators. In this paper, we revisit the perspective and methodology on constrained optimization paradigms for kinematic control of redundant manipulators. [ABSTRACT FROM AUTHOR]

Displacements of the base during trajectory tracking are a common issue in the control of aerial manipulators. These are caused by reaction torques transferred to the base due to the manipulator motion and, in particular, to the motion of its center of gravity. We present a novel approach to reduce base displacements of a kinematically redundant aerial manipulator by using null-space projection in the inverse kinematic control. A secondary objective function minimizes the horizontal displacement of the manipulator center of gravity. We test this algorithm on different trajectories for both three and four degrees of freedom (DOF) manipulators in a simulation environment. The results comparing our algorithm with inverse kinematic control without the null-space projection show up to an 80% reduction in the end-effector position error and an average of about 56% reduction in maximum base displacement. The simulation implementation also runs faster than in real-time in our code implementation. We provide a workspace analysis based on multiple stopping criteria such as excessive base displacement, joint velocities and end-effector position error for the 3 and 4 DOF manipulators. As expected, the 4 DOF manipulator has a larger workspace.

Medication delivery robots, as a type of service robots, are becoming increasingly important in the field of helping the elderly and disabled. Conventional drug delivery robots focus on mobility and often lack the ability to pick and place drugs independently. Based on this phenomenon, a new medication delivery robot structure based on a 3-DOF robot manipulator is designed in this paper. According to the motion process, the 3-DOF manipulator is modeled and simulated. Meanwhile, the influence of different trajectory planning methods on the drug taking and discharging process are compared, which provides theoretical and technical support for the research of robots in related fields.

Arm cycling on an ergometer is common in sports training and rehabilitation protocols. The hand movement is constrained along a circular path, and the user is working against a resistance, maintaining a cadence. Even if the desired hand trajectory is given, there is the flexibility to choose patterns of joint coordination and muscle activation, given the kinematic redundancy of the upper limb. With changing external load, motor noise and changing joint stiffness may affect the pose of the arm even though the endpoint trajectory is unchanged. The objective of this study was to examine how the crank resistance influences the variances of joint configuration and muscle activation. Fifteen healthy participants performed arm cranking on an arm-cycle ergometer both unimanually and bimanually with a cadence of 60 rpm against three crank resistances. Joint configuration was represented in a 3-dimensional joint space defined by inter-segmental joint angles, while muscle activation in a 4-dimensional "muscle activation space" defined by EMGs of 4 arm muscles. Joint configuration variance in the course of arm cranking was not affected by crank resistance, whereas muscle activation variance was proportional to the square of muscle activation. The shape of the variance time profiles for both joint configuration and muscle activation was not affected by crank resistance. Contrary to the prevailing assumption that an increased motor noise would affect the variance of auxiliary movements, the influence of noise doesn't appear at the joint configuration level even when the system is redundant. Our results suggest the separation of kinematic- and force-control, via mechanisms that are compensating for dynamic nonlinearities. Arm cranking may be suitable when the aim is to perform training under different load conditions, preserving stable and secure control of joint movements and muscle activations. [ABSTRACT FROM AUTHOR]

SCARA robot is one of the most popularly used robots in industry. The obstacle avoidance feature of multiple SCARA robot collaboration is essential and prominent, which can be used to support multiple robots to accomplish not only more sophisticated tasks but also more efficient than individual robot. This paper mainly focuses on studying the problem of simultaneous multi-robot coordination and obstacle avoidance. A cooperative kinematic control problem of multiple robot manipulators, collision avoidance is taken into account to be the primary task as an inequality constraint and trajectory planning task is considered to be the secondary objective as to ensure the priority of safety, is described as a quadratic programming (QP) problem. Then, a recurrent neural network (RNN) based dynamic controller is designed to solve the formulated QP problem recursively. The convergence of the designed neural network is proved through Lyapunov analysis. With three SCARA planar robots, the effectiveness of the proposed controller is validated through numerical simulations. As observed in the results, when the minimal distance between robots is less than the setting safety distance, the collision avoidance strategy reacts to impel robots to avoid collision, which achieves the primary objective for obstacle avoidance; otherwise, the robot performs the desired trajectory tracking task. [ABSTRACT FROM AUTHOR]

For redundant robots, fault tolerant kinematic control can be formulated as a time-variant quadratic programming (QP). For solving a time-variant QP, traditional recurrent neural networks (RNNs) often require multiple iterations to solve one sampled time-invariant QP at each time instant. As a variant of RNNs, zeroing neural networks (ZNNs) overcome this limitation by leveraging the time derivatives of coefficients. However, conventional ZNNs cannot handle bound constraints directly and still require to convert bound constraints into inequality constraints. To avoid multiple iterations at each time instant and the conversion from bound constraints into inequality constraints, this paper proposes a linear-variational-inequality (LVI) based zeroing neural network (LVI-ZNN). The LVI-ZNN is developed by integrating the ZNN design methodology with the design procedure of the LVI-based primal–dual neural network (LVI-PDNN). The proposed LVI-ZNN combines the strengths of ZNNs and the LVI-PDNN, thereby requiring only a single iteration at each time instant and eliminating the need to convert bound constraints into inequality constraints. The global convergence of the LVI-ZNN is theoretically analyzed and proved. Comparative simulative studies demonstrate the efficacy and superiority of the LVI-ZNN with merits such as high solution accuracy, fewer neurons and no extra hyper-parameters. Finally, physical experimentation is conducted on a real Franka Emika Panda robot, validating the effectiveness of the LVI-ZNN in real-time fault tolerant kinematic control of redundant robots. [ABSTRACT FROM AUTHOR]

Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs. However, redundant manipulators are coupled highly-nonlinear systems which exert difficulty of redundancy resolution computation. Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits. In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit. Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles. The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.

Recently, continuum flexible robots have been designed for the use in diverse applications; including the exploration of confined static and dynamic environments. One of the challenging tasks for those robots is planning optimal trajectories due to, not only the redundant Degrees of Freedom (DOF) they own but also their compliant behaviour. In this paper, an Imitation-based Pose Planning (IbPP) approach is proposed to teach a two-section continuum robot the motion primitives that will facilitate achieving and generalizing for spatial point-to-point motion which involves both position and orientation goals encoded in a dual quaternion form. Two novel approaches are proposed in this research to intuitively generate the motion demonstrations that will be used in the proposed IbPP. Namely, a flexible input interface, acting as a twin robot, is designed to allow a human to demonstrate different motions for the robot end-effector. Alternatively, as a second approach, the Microsoft Kinect sensor is used to provide motion demonstrations faster via human arm movements. Based on the kinematic model of the two-section continuum robot, a Model Reference Adaptive Control (MRAC) algorithm is developed to achieve tracking the generated trajectory from the IbPP and to guarantee the robustness against the model uncertainties and external disturbances. Moreover, controller stability analysis is developed based on Lyapunov criteria. Finally, simulations are conducted for the two-section continuum robot to prove the ability of the proposed IbPP with the two proposed inputs to learn and generalize for spatial motions, which in future could be easily accommodated for robots with multiple sections. In addition, the proposed MRAC shows a significant performance towards following the required trajectory and rejecting the external disturbance.