The method of parametric optimization is applied to the energy analysis of the motion of three-link manipulators. The quality criterion is the energy consumption along 1 m of the path. It consists of two parts: 1) The sum of the products of joint moments and relative joint velocities (an equivalent of the mechanical work); 2) a quadratic form of joint velocities (an analogue of dissipation losses). Typical spatial motions are studied: the hand of the robot moves along a straight line from an initial position to another one, and returns to the start. Several velocity function shapes (parabolic, sinusoidal, and triangular), both symmetric and non-symmetric ones, are considered. The dependence of the energy consumption on the velocity form, on the trajectory parameters and on mechanical and geometrical characteristics of links is discussed.

This paper presents a method to obtain near minimum-time trajectories for two coordinated manipulators handling a rigid object. A piece-wise constant function is used to approximate the second derivatives of the generalised coordinates of the manipulators of the system. This transforms the time optimal control problem into a non-linearly constrained optimisation problem. The transformed problem is then solved by the sequential quadratic programming technique. A numerical example involving two SCARA type manipulators handling a long beam is used to illustrate the proposed scheme.

Many of the advanced driver assistance systems have the goal of perceiving the surroundings of a vehicle. One of them, adaptive cruise control, takes charge of searching for other vehicles in order to detect and track them with the aim of maintaining a safe distance and to avoid dangerous maneuvers. In the research described in this article, this task is accomplished using an on board camera. Depending on when the vehicles are detected the system analyzes movement or uses a vehicle geometrical model to perceive them. After, the detected vehicle is tracked and its behavior established. Optical flow is used for movement while the geometric model is associated with a likelihood function that includes information of the shape and symmetry of the vehicle and the shadow it casts. A genetic algorithm finds the optimum parameter values of this function for every image. As the algorithm receives information from a road detection module some geometric restrictions are applied. Additionally, a multiresolution approach is used to speed up the algorithm. Examples of real image sequences under different weather conditions are shown to validate the algorithm.

The axial offset joint has two rotating axes that do not intersect but have a specific offset in space. It is used widely in parallel manipulators (PMs). The offset-joint workspace can directly affect the PM workspace. This study performed a theoretical derivation and workspace analysis of a class of axial offset joints. First, a theoretical parametric model describing the rotation range of the offset joint is established that considers the interference of the offset joint because of the contact between the upper- and lower-joint brackets during movement. Second, the analytical expressions of the offset-joint workspace are formulated based on the coordinate system transformation. The offset-joint workspace is theoretically calculated in this study using formulations. Then, through a comparative analysis, the superiority of the offset joint compared with the universal joint is verified. The theoretical formulations in this paper can be used to calculate the workspace of a class of axial offset joints. Finally, based on a workspace analysis of three types of PMs using offset, universal, and spherical joints, the offset-joint PM workspace is much larger than those of the other two types.

In this paper, the realization of any specified planar Cartesian compliance for an object grasped by a compliant hand is addressed. The hands considered have 2 or more fingers for which each has 3 modulated elastic joints and predetermined link lengths. Geometric construction-based compliance synthesis procedures are developed. Using these procedures, a large set of compliant behaviors can be realized by a single hand simply by adjusting the configuration of each finger and by adjusting the joint stiffness (using variable stiffness actuation) of each finger joint.

This paper presents a new cooperative object transportation technique using parallel line formation with a circular shift. Typical areas of research in relation to object transportation are grasping, pushing, and caging techniques, but these require precise grasping behaviors, iterative motion correction according to the object pose, and the real-time acquisition of the object shape, respectively. In this paper, the proposed technique does not need to consider the shape or the pose of objects, and equipped tools are not necessary for object transportation because objects are transported by pushing behavior only. Multiple robots create parallel line formation using a virtual electric dipole field and then push multiple objects into the formation. This parallel line is extended to the goal using cyclic motion by the robots and the objects are transported to the goal by pushing behavior. The above processes are decentralized and activated based on the finite state machine of each robot. Simulations and practical experiments are presented to verify the proposed technique.

There are few studies on the intelligent guidance of unmanned sailboats, which should coordinate pluralistic tasks at sea in the nature of its maneuvring intractability. To ensure the algorithmic practicability, this paper proposes a path-following and collision-avoidance guidance approach of unmanned sailboats with total formulaic description. The risk-detecting mechanism is fabricated by setting a circular detecting zone and using the time to the closest point of approach. Then, the risk of collision, the path deviation, the speed loss, and the course loss can be judged by constructing the cost functions and applying the distance to closest point of approach. The optimized heading angle is deemed as the one minimizing the aggregate cost functions, which is sought by applying and improving the beetle antennae search (BAS) algorithm. In the proposed modified BAS, the searching step is redesigned to enhance the searching efficiency. To ensure the convergence of the real heading angle to the reference, the backstepping-based control law is fabricated for the high-order sailboat model and in the linear form. The control parameters are offline optimized through the modified BAS. Compared with the adaptive control, this controller can guarantee more computation simplicity and the optimized control performance. Finally, simulation corroborates that the sailboat can successfully complete path following and collision avoidance while encountering multiple static and moving obstacles under the proposed schemes.

It is well known that computed torque robot control is subjected toperformance degradation due to uncertainties in robot model, and application ofneural network (NN) compensation techniques are promising. In this paper weexamine the effectiveness of neural network (NN) as a compensator for thecomplex problem of Cartesian space control. In particular we examine thedifferences in system performance of accurate position control when the same NNcompensator is applied at different locations in the controller structure. It isfound that using NN to modify the reference trajectory to compensate for modeluncertainties is much more effective than the traditional approach of modifyingcontrol input or joint torque/force. To facilitate the analysis, a new NNtraining signal is introduced and used for all cases. The study is also extendedto non-model based Cartesian control problems. Simulation results withthree-link rotary robot are presented and performances of different compensatinglocations are compared.

Developed minimally invasive surgical (MIS) robots are large multi-arm, multipurpose systems requiring significant investments that limit their availability in hospitals. A larger distribution of MIS robots with benefit for patients might be achieved improving their modularity and scalability so that smaller hospitals or medical centers could decide for a simpler and lower cost setup for a limited number of treatments only, while centers with higher funding could have more systems dedicated to different classes of operations. In line with this statement the paper proposes the paradigm of lean MIS system comprising a scalable set of modular, agile, small size single-instrument robots with limited life cycle cost. Miniaturization of instruments can further reduce invasiveness of procedures and one promising research direction is needle laparoscopic surgery, which can be applied to classes of operations on small regions requiring small force interaction with the patient. In the paper the development of a lean single-instrument manipulator for needlescopic surgery is presented and a new master concept for accurate restitution of surgical force proposed and discussed.

Collision-Avoidance is a key issue in planning trajectories for dual robots whose workspaces overlap. In this paper, we develop a new trajectory planning method by proposing a traffic control schemes. The traffic controller determines the next positions for each robot based on the motion priority and path direction subject to the collision-avoidance conditions and the robots' physical limits. The problem of determining the next positions is formulated and optimized. Algebraic expressions for collision avoidance between every-pair of links – one from the first robot and the other from the second robot – are derived in configuration space. These algebraic expressions are then used to solve the problem of determining “optimal” (in the sense of path direction and motion priority) robots' trajectories. A solution procedure is developed using a nonlinear programming (NLP) solver. The main advantage of our approach is that the two robots' trajectories can be determined simultaneously without requiring any a priori path information. Several numerical examples are presented to demonstrate the validity and effectiveness of the proposed approach.

A geometrical methodology for automatic machine tool command generation is introduced. The geometric entities relevant to machine tool operation are unambiguously defined by volume models. A vector sum operator is used to determine regions of the configuration space of the machine tool which correspond to undesired collisions, cavity access and material removal. Some indication of how cutter paths are generated is given and a practical implementation for the case of machining two and a half dimensional components under two and a half axis control is discussed in more detail.

Twelve equations are presented for describing how robots move through their environments receiving information and producing required results. There is a total of 16 variables, and the sets of independent and dependent variable for the system of equations are presented. Graphs and example uses are also presented.

Recently, three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding, object reconstruction as well as computer vision for robotic Systems. The image generation problem and the model generation problem are presented. More specifically, the inputs to the image generator are an old image, object model, motion specification, and hidden line and hidden surface algorithms. The output is a new image. Since the object model is given, the top-down approach is usually used. On the other hand, for the model generation problem, the input is an image sequence while the output is an object model. Since the object model is not given, and bottom-up approach is usually used.

In this paper, the largest possible object approach is proposed and the advantages of this approach are stated. They are:

1. This approach may be applicable to objects with planar surfaces as well as nonplanar surfaces.

2. This approach may be applicable to the case that there are more than one face change per frame.

3. This approach may be applicable when the camera is moving.

4. This approach may be applicable when the object is measured by several measuring stations.

By using this approach, algorithms for simple object reconstruction given a sequence of pictures are presented together with illustrative examples. The relevance and importance of this work are discussed.

The results of this paper may have useful applications in object reconstruction, pictorial data reduction and computer vision for robotic Systems.

The phenomenal growth in the utilization of commercial unmanned aerial vehicles (UAVs) or drones leads to an urgent need for new approaches to ensure safety in the sky. Effective aerial surveillance requires patrolling swarms to react according to the various behaviors demonstrated by intruding swarms, but existing approaches are not practical when dealing with a large number of drones. Specifically, predicting the behaviors or planned paths of the intruding swarms is highly challenging as intruders may perform evasive strategies to avoid detection. Therefore, this work utilizes heuristic search strategies and investigates how various intruder behaviors affect the search performance. To investigate the search performance, a swarm versus swarm simulator is developed. Using the simulator, first, a comparative study is performed to evaluate how intruders’ behaviors can affect the performance of the patrolling swarm. Subsequently, three approaches, including single-objective optimization, multi-objective optimization, and Lévy flight, are compared in terms of their detection performance in a bounded space. The results suggest that multi-objective optimization outperforms both single-objective optimization and Lévy flight-based approaches. Furthermore, our results show that intruders have a lower chance of being tracked when moving in a dense crowd, and this finding reaffirms the schooling behaviors of fish. In a specific simulation scenario, the total percentage of detection is above 90%. However, the detection percentage is highly related to other factors such as search space, number of patrolling UAVs, and the intruders’ behaviors.

Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are convolutional neural network (CNN)-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: (a) their high dimension and (b) lack of generalization, leading to low efficiency and poor performance in real robotic applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in four challenging real-world datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. The code of our work is publicly available at https://github.com/MedlarTea/CAE-VPR.

We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

A spring-assisted modular and reconfigurable robot (SA-MRR) has been recently developed at our laboratory to reinforce its performance and enable safe and dexterous operation in human–robot environments. Multiple working mode (MWM) control enables each SA-MRR joint module to switch independently between working in a primary actuation mode and a secondary, spring-assisted mode that may improve task-specific energy performance measures and safety in a variety of manipulation tasks. The spring-assisted mode is characterized by synergy of spring and motor energy and may be summoned to offset motor energy demands or to safeguard a reconfigurable set of secondary joint limits. In this research work, two spring-assisted working mode strategies are proposed, and their characteristics have been investigated for SA-MRR actuation energy advantages while safe robot segregation in collaboration tasks is maintained. One MWM strategy has been designed to safeguard task-specific joint limits and is able to decrease motor energy consumption in some tasks. Another MWM strategy has been designed for energy efficiency and was able to reduce motor energy per cycle by $ \text{72}$ % in a simulated manipulation task while maintaining spatial safety constraints. Numerical simulations have demonstrated the effectiveness of the proposed spring-assisted working mode strategies for energy-aware safe manipulation applications.

Microrobots with their promising applications are attracting a lot of attention currently. A microrobot with a triangular mechanism was previously proposed by scientists to overcome the motion limitations in a low-Reynolds number flow; however, the control of this swimmer for performing desired manoeuvres has not been studied yet. Here, we have proposed some strategies for controlling its position. Considering the constraints on arm lengths, we proposed an optimal controller based on quadratic programming. The simulation results demonstrate that the proposed optimal controller can steer the microrobot along the desired trajectory as well as minimize fluctuations of the actuators length.