Contact force is one of the most significant feedback for robots to achieve accurate control and safe interaction with environment. For continuum robots, it is possible to estimate the contact force based on the feedback of robot shapes, which can address the difficulty of mounting dedicated force sensors on the continuum robot body with strict dimension constraints. In this paper, we use local curvatures to estimate the magnitude and location of single or multiple contact forces based on Cosserat rod theory. We validate the proposed method in a thin elastic tube and calculate the curvatures via Fiber Bragg Grating (FBG) sensors or image feedback. For the curvature feedback obtained from multicore FBG sensors, the overall force magnitude estimation error is $0.062 \pm 0.068$ N and the overall location estimation error is $3.51 \pm 2.60$ mm. For the curvature feedback obtained from image, the overall force magnitude estimation error is $0.049 \pm 0.048$ N and the overall location estimation error is $2.75 \pm 1.71$ mm. The results demonstrate that the curvature-based force estimation method is able to accurately estimate the contact force.

This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT* algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success.

The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

Minimally invasive surgery (MIS) has been an essential tool in the surgical sector for many years due to its crucial advantages compared to open surgery. To overcome remaining limitations, teleoperated MIS experienced a strong emergence. However, the widespread usage of such systems is hindered by the enormous financial hurdle. The use of standard components and conventional tools for teleoperated MIS can facilitate integration into existing hospital workflows and can be a cost-efficient and versatile approach for research purposes. To compensate for the lack of haptic feedback, some teleoperation setups inherit a sensor system allowing them to record interaction forces and display them at the user interface. In research and in commercially available systems, different positions for the sensor can be found. In this paper, mechanical interfaces for the guidance and actuation of non-wristed and wristed standard instruments are presented. Furthermore, a method for the extracorporeal measurement of interaction forces is presented, characterized, and discussed. The overall mean relative error of the magnitude of the interaction force is 9.4%, while the overall mean absolute error of the force vector is 14.4$^{\circ }$, both below the respective human differential perception threshold. The presented measurement method is a simple, yet sufficiently accurate approach to measure interaction forces in surgical telemanipulation.

The identification of nonlinear terms existing in the dynamic model of real-world mechanical systems such as robotic manipulators is a challenging modeling problem. The main aim of this research is not only to identify the unknown parameters of the nonlinear terms but also to verify their existence in the model. Generally, if the structure of the model is provided, the parameters of the nonlinear terms can be identified using different numerical approaches or evolutionary algorithms. However, finding a non-zero coefficient does not guarantee the existence of the nonlinear term or vice versa. Therefore, in this study, a meticulous investigation and statistical verification are carried out to ensure the reliability of the identification process. First, the simulation data are generated using the white-box model of a direct current motor that includes some of the nonlinear terms. Second, the particle swarm optimization (PSO) algorithm is applied to identify the unknown parameters of the model among many possible configurations. Then, to evaluate the results of the algorithm, statistical hypothesis and confidence interval tests are implemented. Finally, the reliability of the PSO algorithm is investigated using experimental data acquired from the UR5 manipulator. To compare the results of the PSO algorithm, the nonlinear least squares errors (NLSE) estimation algorithm is applied to identify the unknown parameters of the nonlinear models. The result shows that the PSO algorithm has higher identification accuracy than the NLSE estimation algorithm, and the model with identified parameters using the PSO algorithm accurately calculates the output torques of the joints of the manipulator.

There are various matrices to represent parallel mechanisms. It is essential to design a kind of approach to not only denote the parallel structures but also disclose the joint directions. In this paper, a novel methodology called the kinematic joint matrix (KJM) is proposed. It possesses the mapping relations with parallel manipulators with three kinds of kinematic joints. The size of such matrix is smaller when compared with that of topology matrix. A series of two to six degrees-of-freedom parallel architectures is denoted by the KJM. A convenient approach using a special block diagram is introduced to distinguish various kinds of kinematic joint matrices. In addition, detailed comparisons between KJM and topology matrix are investigated. Three regulations are proposed for the latter to be applicable to parallel mechanisms.

We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path planning task into different levels of sub-tasks in complex environments and solves each sub-task in a smaller state subspace to decrease the state space dimensions. The HCNM model imitates animal asymptotic properties through the study of thermodynamic processes and designs a cognitive learning algorithm to achieve online optimum search strategies. We prove that the learning algorithm designed ensures that the cognitive model can converge to the optimal behavior path with probability one. Robot navigation is studied on the basis of the cognitive process. The experimental results show that the HCNM model has strong adaptability in unknown and environment, and the navigation path is clearer and the convergence time is better. Among them, the convergence time of HCNM model is 25 s, which is 86.5% lower than that of HRLM model. The HCNM model studied in this paper adopts a hierarchical structure, which reduces the learning difficulty and accelerates the learning speed in the unknown environment.

Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.

Dielectric elastomers (DEs) find applications in many areas, particularly in the field of soft robotics. When modeling and simulating DE-based actuators and sensors, a substantial portion of the literature assumes the selected DE material to behave in some perfectly hyperelastic manner, and the vast majority have assumed invariant permittivity. However, studies on simple planar DEs have revealed instabilities and hastened breakdowns when a variable permittivity is allowed. This is partly due to the intertwined electromechanical properties of DEs rooted on their labyrinthine polymeric microstructures. This work focuses on studying the effects of a varying (with stretch) permittivity on the out-of-plane deformation of a circular DE, using a model derived from principles of strain-induced polymer birefringence. In addition, we utilize the Edward–Vilgis model, which attempts to account for effects related to crosslinking, and length extension, slippage, and entanglement of polymer chains. Our approach reveals the presence of “stagnation” regions in the electromechanical behavior of the DE actuator material. These stagnation regions are characterized by both electrical and mechanical critical electrostrictive coefficient ratios. Mechanically, certain values of the electrostrictive coefficient ratio predict cases where deformation does not occur in response to a change in voltage. Electrically, certain cases are predicted where changes in capacitance cannot be measured in response to changes in deformation. Thus, some combined conditions of loading and material properties could limit the effectiveness of DE membranes in either actuation or sensing. Therefore, our results reveal mechanisms that could be useful to designers of actuators and sensors and unveil an opportunity for exploring new theoretical materials with potential novel applications. Furthermore, since there are known analogous formulations between electrical and optical properties, criticality principles studied in this article could be extended to optomechanical coupling.

Earth-contact mechanism (ECM), a type of mechanism to keep the system in contact with the earth and to move with the terrain changes. This paper uses the virtual equivalent parallel mechanism (VEPM) to convert the terrain data into the kinematical variables of the moving platform in the VEPM, and further analyzes the performance of the VEPM at each terrain point. Then, the comprehensive performance of the VEPM is chosen as the optimization goal, and a task-oriented dimensional optimization approach combined with the particle swarm algorithm and the neural network algorithm is proposed. This paper conducted a comparative experiment to verify the superiority of the new approach in optimizing the ECM’s comprehensive performance, whose performance analysis also can be applied into the layout design of the ECM. This paper proposed an analysis method to construct the ECM’s performance map based on the digital terrain map, which helps the control system and operator to make the optimal control decision.

Productivity can be increased by manipulators tracking the desired trajectory with some constraints. Humans as moving obstacles in a shared workspace are one of the most challenging problems for cable-driven parallel mechanisms (CDPMs) that are considered in this research. One of the essential primary issues in CDPM is collision avoidance among cables and humans in the shared workspace. This paper presents a model and simulation of a reconfigurable, fully constrained CDPM enabling detection and avoidance of cable–human collision. In this method, unlike conventional CDPMs where the attachment points are fixed, the attachment points on the rails can be moved (up and down on their rails), and then the geometric configuration is adapted. Karush–Kuhn–Tucker method is proposed, which focuses on estimating the shortest distance among moving obstacles (human limbs) and all cables. When cable and limbs are close to colliding, the new idea of reconfiguration is presented by moving the cable’s attachment point on the rail to increase the distance between the cables and human limbs while they are both moving. Also, the trajectory of the end effector remains unchanged. Some simulation results of reconfiguration theory as a new approach are shown for the eight-cable-driven parallel manipulator, including the workspace boundary variation. The proposed method could find a collision-free predefined path, according to the simulation results.

In this work, a vision-based guidance algorithm for interception of an aerial manoeuvring target is proposed. Vision-based aerial engagement in 3D space in an outdoor environment is challenging due to uncertainty in estimating the target location. The vision-based guidance algorithm proposed here generates the desired velocity commands for the interceptor drone based on the orientation of the target pixel in the image plane. The desired velocity at the centre of gravity of the interceptor is calculated such that the velocity at the camera centre is aligned towards the line joining the target pixel and the camera centre. It is shown that the proposed guidance strategy forces the target pixel towards the centre of the image plane and leads to low miss distance. The algorithm developed is first tested in the ROS-Gazebo platform and then implemented in field experiments.

This paper presents an investigation of the effectiveness of different inverse kinematics strategies in a context of physical human-robot interaction in which passive articulated shells are mounted on the links of a serial robot for manual guidance. The concept of passive link shells is first recalled. Then, inverse kinematics strategies that are designed to plan the trajectory of the robot according to the motion sensed at the link shells are presented and formulated. The different approaches presented all aim at interpreting the motion of the shells and provide an intuitive interaction to the human user. Damped Jacobian based methods are introduced in order to alleviate singularities. A serial 5-dof robot used in previous work is briefly introduced and is used as a test case for the proposed inverse kinematics schemes. The robot includes two link shells for interaction. Simulation results based on the different inverse kinematic strategies are then presented and compared. Finally, general observations and recommendations are discussed.

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.

Both prosthetic and robotic research communities have tended to focus on hand/gripper development. However, the wrist unit could enable higher mobility of the end effector and thus more efficient and dexterous manipulation. The current state of the art in both prosthetic and robotic wrists is reviewed systematically, mainly concerning their kinematic structures and resultant capabilities. Further, by considering the biomechanical advantages of the human wrist, an evaluation including the mobility, stability, output capability, load capacity and flexibility of the current artificial wrists is conducted. With the pentagonal capability radar charts, the major limitations and challenges in the current development of artificial wrists are derived. This paper hence provides some useful insights for better robotic wrist design and development.

Body posture determination methods have many applications, including product design, ergonomic workplace design, human body simulation, virtual reality, and animation industry. Initiated in robotics, inverse kinematic (IK) method has been widely applied to proactive human body posture estimation. The analytic inverse kinematic (AIK) method is a convenient and time-saving type of IK methods. It is also indicated that, based on AIK methods, a specific body posture can be determined by the optimization of an arbitrary objective function. The objective of this paper is to predict the postures of human arms during reaching tasks. In this research, a human body model is established in MATLAB, where the middle rotation axis analytic kinematic method is accomplished, based on this model. The joint displacement function and joint discomfort function are selected to be initially applied in this AIK method. Results show that neither the joint displacement function nor the joint discomfort function predicts postures that are close enough to natural upper limb postures of human being, during reaching tasks. Therefore, a bi-criterion objective function is proposed by integrating the joint displacement function and joint discomfort function. The accuracy of the arm postures, predicted by the proposed objective function, is the most satisfactory, while the optimal value of the coefficient, in the proposed objective function, is determined by golden section search.

This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.

This paper proposes a modular method based on the kinematics of serial manipulators to synthesize over-constrained mechanisms. Because the PPP manipulator has an unlimited work space, its end-effector can be constrained to trace a trajectory identical to those of another open-chain manipulator, including a P joint single link and an RR dyad. In doing so, two open-chain manipulators can be concatenated to form closed-loop mechanisms, including PPPP, PPPRR, or PPCR mechanisms. To design over-constrained mechanisms efficiently, the Denavit–Hartenberg convention is adopted to describe the PPP manipulator kinematically, and the Euler angles are utilized to derive geometric constraints of synthesized over-constrained mechanisms. Next, kinematic equations of the PPP manipulator can be modularized and applicable to analyze different closed-loop mechanisms. At last, by adjusting link lengths, twisted angles, and joint angles of the synthesized PPPRR and PPCR mechanisms to form other over-constrained mechanisms configurationally. The novelty of this research lies in modularizing the over-constrained mechanism into two movable serial manipulators whose end-effectors share identical trajectory and orientation. Thus, defining geometrical constraints of the over-constrained mechanism can be transformed into finding angular parameters describing the orientation of these two serial manipulators such that the end-effector coordinate system of two manipulators can properly be aligned. Angular parameters of the serial manipulators can be easily determined by means of Euler angles, which yields an advantage of easy calculation since it only involves the computation of Euler angles parameters. The presented method can be extended to the kinematic synthesis and analysis of more spatial closed-loop mechanisms.

The rotational joint plays a vital role in the industrial and civil areas, which is typically utilized to achieve the relative rotation between the adjacent parts. Generally, structuring a conventional rotational joint involves the bulk actuators (e.g., motor and hydraulic cylinders) and complex structures, bringing difficulty for miniaturing the dimension. In this paper, a class of novel rotational mechanisms, which were constructed by the combination of compliant mechanisms (e.g., cartwheel pivot and multileaf pivot) and intelligent actuator (e.g., shape memory alloy (SMA) wire and spring), was proposed to reduce the complexity of the conventional rotational joints. As the case study, a novel SMA wire-driven flexural rotational mechanism (SDFRM), which is constructed by the cartwheel pivot and SMA wire, was developed to demonstrate the feasibility of combining the compliant mechanism and smart actuator. After establishing the static model of the cartwheel pivot and the thermal effect model of the SMA wire, the overall model of SDFRM was developed for the comprehensive performance analysis and the control system design. After that, the model validation and experiments were performed with the proposed prototype and control system. It can be seen from the experimental results that the proposed model can be validated within the error of 3.8%. In addition, the performance study on SDFRM indicates that the prototyped SDFRM system can track the given trajectories within the error of 0.2 mm in the workspace. As a result, the proposed concept was demonstrated as an effective way to reduce the dimension and weight of the conventional rotational joint.

In this paper, we propose a set of robust training methods for deep reinforcement learning to transfer learning acquired in one control task to a set of previously unseen control tasks. We improve generalization in commonly used transfer learning benchmarks by a novel sample elimination technique, early stopping, and maximum entropy adversarial reinforcement learning. To generate robust policies, we use sample elimination during training via a method we call strict clipping. We apply early stopping, a method previously used in supervised learning, to deep reinforcement learning. Subsequently, we introduce maximum entropy adversarial reinforcement learning to increase the domain randomization during training for a better target task performance. Finally, we evaluate the robustness of these methods compared to previous work on simulated robots in target environments where the gravity, the morphology of the robot, and the tangential friction coefficient of the environment are altered.