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This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests. [ABSTRACT FROM AUTHOR]

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms. [ABSTRACT FROM AUTHOR]

During the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method. [ABSTRACT FROM AUTHOR]

In order to improve the speed of motion planning, this paper proposes an improved RRTConnect algorithm (SDPS-RRTConnect) based on sparse dead point saved strategy. Combining sparse expansion strategy and dead point saved strategy, the algorithm can reduce the number of collision detection, fast convergence, avoid falling into local minimum, and ensure the completeness of search space. The algorithm is simulated in different environments. The results show that in complex environments, the sparse dead point saved strategy plays a good effect. In simple environments, the greedy connection strategy works well. Compared with the standard RRT algorithm, the standard RRTConnect algorithm, and the SDPS-RRT algorithm, the SDPS-RRTConnect algorithm has the shortest planning time, and it is suitable for both simple and complex environments. The 500 experiments show that the algorithm has strong robustness. The actual robot experiments show that the path planned by SDPS-RRTConnect algorithm is effective. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown. [ABSTRACT FROM AUTHOR]

Summary: Humanoid robots (HR) equipped with a sophisticated facial character analysis (FCA) algorithm can able to initiate crucial improvements in human–robot interactions. This paper, for the first time in the literature, proposes a three-stage FCA algorithm for the HR. At the initial stage of this algorithm, the HR detects the face with the Viola–Jones algorithm, and then important facial distance measurements are obtained with the geometric-based facial distance measurement technique. Finally, the measured facial distances are evaluated with the physiognomy science to reveal the characteristic properties of a person. Even though the proposed algorithm can be implemented to all HR, in this paper, it has been specifically applied to NAO HR. The reliability of the developed FCA algorithm is verified by analyzing each terminal decision about the character and its connection with the measured facial distances in the anatomy science. [ABSTRACT FROM AUTHOR]

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The repetitive motion planning movements of the redundant manipulator will cause oscillations and unintended swings of joints, which increase the risk of collisions between the manipulator and its surroundings. Motivated by this phenomenon, this paper presents an inverse kinematics algorithm for the spherical-revolute-spherical manipulator to solve the paradox raised by joint-drift and control the pose with no swing of the elbow. This algorithm takes the joint Cartesian positions set as the intermediary and divides the inverse solution process into two mapping processes within joint limits. Simulations are executed to evaluate this algorithm, and the results show this algorithm is applicable to repetitive motion planning and is capable of producing superior configurations based on its real-time ability and stable solve rate. Experiments using the 7-degree-of-freedom spherical-revolute-spherical manipulator demonstrate the effectiveness of this algorithm to remedy the joint-drift and elbow swing compared to Kinematics and Dynamics Library and TRAC-IK. [ABSTRACT FROM AUTHOR]

On-orbit servicing and active debris removal missions will rely on the use of unmanned satellite equipped with a manipulator. Capture of the target object will be the most challenging phase of these missions. During the capture manoeuvre, the manipulator must avoid collisions with elements of the target object (e.g., solar panels). The dynamic equations of the satellite-manipulator system must be used during the trajectory planning because the motion of the manipulator influences the position and orientation of the satellite. In this paper, we propose application of the bidirectional rapidly exploring random trees (BiRRT) algorithm for planning a collision-free trajectory of a manipulator mounted on a free-floating satellite. A new approach based on pseudo-velocities method (PVM) is used for construction of nodes of the trajectory tree. Initial nodes of the second tree are selected from the set of potential final configurations of the system. The proposed method is validated in numerical simulations performed for a planar case (3-DoF manipulator). The obtained results are compared with the results obtained with two other trajectory planning methods based on the RRT algorithm. It is shown that in a simple test scenario, the proposed BiRRT PVM algorithm results in a lower manipulator tip position error. In a more difficult test scenario, only the proposed method was able to find a solution. Practical applicability of the BiRRT PVM method is demonstrated in experiments performed on a planar air-bearing microgravity simulator where the trajectory is realised by a manipulator mounted on a mock-up of the free-floating servicing satellite. [ABSTRACT FROM AUTHOR]

SUMMARY: In order to improve the training efficiency and establish a multi-person cooperative training simulation system, including "virtual human," in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced. [ABSTRACT FROM AUTHOR]

This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations. [ABSTRACT FROM AUTHOR]

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm. [ABSTRACT FROM AUTHOR]

To improve the uniformity of coating thickness and spraying efficiency, new point cloud modeling and slicing algorithm are proposed to deal with free-form surfaces for the spray painting robot in this paper. In the process of point cloud modeling, the edge preservation algorithm is firstly presented to avoid damaging the edge characteristic of the point cloud model. For the spraying gun, the coating deposition model on the free-form surface is determined on the basis of the elliptic double β distribution model. Then, the grid projection algorithm is proposed to obtain grid points between adjacent slices on the free-form surface. Based on this, the analytical solution for calculating the coating thickness at each grid point is obtained. The cross-section contour points are obtained by intercepting the point cloud model with several parallel slices, which is important for the trajectory planning of the spray painting robot. Finally, the uniformity of coating thickness is optimized in terms of the moving speed of the spraying gun and the slice thickness. The simulation and numerical experiment results show that the uniformity of coating thickness and spraying efficiency are improved using the proposed point cloud modeling and slicing algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic algorithm based on the concept of interval analysis to obtain the maximal singularity-free circle or sphere within the workspace of parallel mechanisms. As case studies the 3-RPR planar and 6-UPS parallel mechanisms are considered to illustrate the relevance of the algorithm for 2D and 3D workspaces. To this end, the main algorithm is divided into four sub-algorithms, which eases the understanding of the main approach and leads to a more effective and robust algorithm to solve the problem. The first step is introduced to obtain the constant-orientation workspace and then the singularity locus. The main purpose is to obtain the maximal singularity-free workspace for an initial guess. Eventually, the general maximal singularity-free workspace is obtained. The main contribution of the paper is the proposition of a systematic algorithm to obtain the maximal singularity-free circle/sphere in the workspace of parallel mechanisms. The combination of a maximal singularity-free circle or sphere with the workspace analysis by taking into account the stroke of actuators, as additional constraint to the latter problem, is considered. Moreover, the center point of the circle/sphere is not restrained to a prescribed point. [ABSTRACT FROM PUBLISHER]

Inverse kinematics solutions are the basis for position and orientation control of automated machines in their Cartesian workspace. This paper presents an efficient and robust inverse kinematics algorithm for a new circumferential drilling machine for aircraft fuselage assembly. After a brief introduction to the circumferential drilling machine and its forward kinematics, the paper discusses the nonlinear optimization method for solving inverse kinematics problems. The objective function is defined as a weighted combination of a position error function and an orientation error function. By representing orientation error as the geodesic distance between two points on a unit sphere, the paper proposes to define the orientation error function by using faithful geodesic distance functions, which are accurate approximations to the geodesic distance when it is small. For increased efficiency, robustness, and easy setting of initial values, the inverse kinematics problem is decomposed into two subproblems. The revolute joint coordinates are obtained by nonlinear optimization, and the prismatic joint coordinates are calculated with closed-form formulas. Numerical experiments show that the objective function defined with faithful geodesic distance functions is effective, and the proposed algorithm is efficient, robust, and accurate. The algorithm has been successfully integrated into the control system of the circumferential drilling machine. Preliminary drilling experiments show that the position accuracy of drilled holes is within ±0.5 mm, which is acceptable for the assembly of large aircrafts. [ABSTRACT FROM PUBLISHER]

In this paper, a new hybrid fault-tolerant control (FTC) strategy based on nonsingular fast integral-type terminal sliding mode (NFITSM) and time delay estimation (TDE) is proposed for a Schönflies parallel manipulator. In order to detect, isolate, and accommodate actuator faults, TDE is used as an online fault estimation algorithm. Stability analysis of the closed-loop system is performed using Lyapunov theory. The proposed controller performance is compared with conventional sliding mode and feedback linearization control methods. The obtained results reveal the superiority of the proposed FTC based on TDE and NFITSM. [ABSTRACT FROM AUTHOR]

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot. [ABSTRACT FROM PUBLISHER]

SUMMARY: A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper deals with motion planning and control problems for a class of partially differentially flat systems. They possess a feature that the derivative of the fiber variable can be represented purely by the base variable and its derivatives. Based on this feature, a Beta function-based motion planning algorithm is proposed with less computational cost compared with the optimal control formulation while providing similar system performance. Then, an adaptive controller is constructed through a function approximation technique-based approach. Finally, the feasibility of the proposed motion planning and control algorithms is verified by simulations. [ABSTRACT FROM AUTHOR]

SUMMARY: Quadrotors are unmanned aerial vehicles with many potential applications ranging from mapping to supporting rescue operations. A key feature required for the use of these vehicles under complex conditions is a technique to analytically solve the problem of trajectory planning. Hence, this paper presents a heuristic approach for optimal path planning that the optimization strategy is based on the indirect solution of the open-loop optimal control problem. Firstly, an adequate dynamic system modeling is considered with respect to a configuration of a commercial quadrotor helicopter. The model predicts the effect of the thrust and torques induced by the four propellers on the quadrotor motion. Quadcopter dynamics is described by differential equations that have been derived by using the Newton–Euler method. Then, a path planning algorithm is developed to find the optimal trajectories that meet various objective functions, such as fuel efficiency, and guarantee the flight stability and high-speed operation. Typically, the necessary condition of optimality for a constrained optimal control problem is formulated as a standard form of a two-point boundary-value problem using Pontryagin's minimum principle. One advantage of the proposed method can solve a wide range of optimal maneuvers for arbitrary initial and final states relevant to every considered cost function. In order to verify the effectiveness of the presented algorithm, several simulation and experiment studies are carried out for finding the optimal path between two points with different objective functions by using MATLAB software. The results clearly show the effect of the proposed approach on the quadrotor systems. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper introduces an intuitive and safe command approach for a quadrotor, where inertial and muscular gestures are used for semi-autonomous flight. A bracelet composed of gyroscopes, accelerometers, and electromyographic sensors is used to detect user gestures, then an algorithm is proposed to interpret the signals as flight commands. The main goal is to provide a wearable, easy-to-handle human–machine interface for users a to safely command this kind of vehicles, even for inexpert operators. Safety measures are incorporated in the scheme to further enhance the user's experience. Experimental tests are performed to validate the proposal. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper addresses the application of a novel elimination algorithm with a newly developed homotopy continuation method (HCM) for forward kinematics of a specific hybrid modular manipulator known as n-(6UPS). First, the kinematic model of n-(6UPS) was extracted using a homogenous transformation matrix method. Then, a novel algebraic elimination algorithm was developed to transform the highly nonlinear proposed kinematic model into a system of polynomial equations for each module. Next, the HCM is considered to solve the system of equations. Comparison of the results from the proposed approach with experimental data and other methods demonstrates the efficiency of the proposed contribution. [ABSTRACT FROM AUTHOR]

This paper addresses the problems of barrier coverage and sweep coverage in a corridor environment with a network of self-deployed mobile autonomous robotic sensors. Using the ideas of nearest neighbor rules and information consensus, we propose a decentralized control law for the robotic sensors to solve the coverage problems. Numerical simulations illustrate the effectiveness of the proposed algorithm. The results in this paper demonstrate that such simple motion coordination rules can play a significant role in addressing the issue of coverage in a mobile robotic sensor network. [ABSTRACT FROM AUTHOR]

A Jacobian-based algorithm that is useful for planning the motion of a floating rigid body operated using two input torques is addressed in this paper. The rigid body undergoes a four-rotation fully reversed (FR) sequence of rotations which consists of two initial rotations about the axes of a coordinate frame attached to the body and two subsequent rotations that undo the preceding rotations. Although a Jacobian-based algorithm has been useful in exploring the inverse kinematics of conventional robot manipulators, it is not apparent how a correct FR sequence for a desired orientation could be found because the Jacobian of FR sequences is singular as well as being a null matrix at the identity. To discover the FR sequences that can synthesize the desired orientation circumventing these difficulties, the Jacobian algorithm is reformulated and implemented from arbitrary orientations where the Jacobian is not singular. Due to the insufficient degrees-of-freedom of four-rotation FR sequences required to achieve all possible orientations, the rigid body cannot achieve certain orientations in the configuration space. To best approximate these infeasible orientations, the Jacobian-based algorithm is implemented in the sense of least squares. As some orientations can never be attained by a single four-rotation FR sequence, two different four-rotation FR sequences are exploited alternately to ensure the convergence of the proposed algorithm. Assuming the orientation is supposed to be manipulated using three input torques, the switching Jacobian algorithm proposed in this paper has significant practical importance in planning paths for aerospace and underwater vehicles which aremaneuvered using only two input torques due to the failure of one of the torque-generation mechanisms. [ABSTRACT FROM AUTHOR]

SUMMARY: In this paper, we propose a novel unified framework for virtual guides. The human–robot interaction is based on a virtual robot, which is controlled by the admittance control. The unified framework combines virtual guides, control of the dynamic behavior, and path tracking. Different virtual guides and active constraints can be realized by using dead-zones in the position part of the admittance controller. The proposed algorithm can act in a changing task space and allows selection of the tasks-space and redundant degrees-of-freedom during the task execution. The admittance control algorithm can be implemented either on a velocity or on acceleration level. The proposed framework has been validated by an experiment on a KUKA LWR robot performing the Buzz-Wire task. [ABSTRACT FROM AUTHOR]

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

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

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

SUMMARY: In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a 'partition and cover' approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2 D ×2 D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy. [ABSTRACT FROM AUTHOR]

Summary: Path planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid called CWave is proposed here. Any-angle attribute implies that the algorithm calculates paths which can include line segments at any angle, as opposed to standard A* that runs on an 8-connected graph, which permits turns with 45° increments only. The key idea of CWave is to abandon the graph model and operate directly on the grid geometry using discrete geometric primitives (instead of individual vertices) to represent the wave front. In its most basic form (CWaveInt), CWave requires only integer arithmetics. CWaveInt , however, can accumulate the distance error at turning points. A modified version of CWave (CWaveFpuSrc) with minimal usage of floating-point calculations is also developed to eliminate any accumulative errors, which is proven mathematically and experimentally on several maps. The performance of the algorithm on most of the tested maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta*, Field A*, ANYA, Block A*, and A* adapted for single-source planning (on maps with lower number of isolated obstacles, CWaveFpuSrc is 2−3 times faster than its fastest tested alternative Block A*). An N -threaded implementation (CWaveN) of CWave is presented and tested to demonstrate an improved performance (multithreaded implementation is 1.5−3 times faster than single-threaded CWave). The paper discusses foundations and experimental validation of CWave, and presents future work to address the limitations of the current implementations and obtain further performance enhancements. [ABSTRACT FROM AUTHOR]

Aiming at problems of low optimization accuracy and slow convergence speed in the gait optimization algorithm of lower limb exoskeleton robot, a novel gait multi-objectives optimization strategy based on beetle swarm optimization (BSO)-elite opposition-based learning (EOL) levy flight foraging (LFF) algorithm was proposed. In order to avoid the algorithm from falling into the local optimum, the EOL strategy with global search capability, the LFF strategy with local search capability and the dynamic mutation strategy with high population diversity were introduced to improve optimization performance. The optimization was performed by establishing a multi-objectives optimization function with the robot's gait zero moment point (ZMP) stability margin and driving energy consumption. The joint comparative tests were carried out in SolidWorks, ADAMS and MATLAB software. The simulation results showed that compared with the particle swarm optimization algorithm and the BSO algorithm, the ZMP stability margin obtained by the BSO-EOLLFF algorithm was increased, and the average driving energy consumption was reduced by 25.82% and 17.26%, respectively. The human-machine experiments were conducted to verify the effectiveness and superiority. The robot could realize stable and smooth walking with less energy consumption. This research will provide support for the application of exoskeleton robot. [ABSTRACT FROM AUTHOR]

Nowadays, with the growing use of atomic force microscope (AFM) nanorobots in the fabrication of nanostructures, research in this area has proliferated. A major limiting factor in the utilization of AFM nanorobots is the lack of real-time monitoring. Computer simulations have been widely used to improve the feasibility of nanoparticles pushing by means of the AFM-based nanorobots. In order to prevent damage to a cantilever during the nanoparticle displacement, it is necessary to come up with an algorithm that can check the feasibility of the manipulation operation with regards to the geometrical constraints of the cantilever and the considered path. The existing models for predicting the feasibility of manipulation processes are limited to 2D manipulation models (spherical nanoparticles), which are incapable of predicting the feasibility of 3D operations (lack of a comprehensive model of 3D cases). In this paper, to predict the feasibility of displacing cylindrical nanoparticles and to consider the path of motion (rough surface), a model has been proposed that can ensure the feasibility of the manipulation operation in advance. Finite element simulation has been employed to obtain the maximum load that can be withstood by a cantilever of a specific geometry. Also, the effect of surface roughness on the critical manipulation force has been explored. Ultimately, an algorithm has been presented for determining the feasibility of the manipulation operation by considering the particle motion path and the cantilever geometry. [ABSTRACT FROM AUTHOR]

This paper presents adaptive impedance controllers with adaptive sliding mode friction compensation for anthropomorphic artificial hand. A five-fingered anthropomorphic artificial hand with multi-sensory and Field-Programmable Gate Arra (FPGA)-based control hardware and software architecture is designed to fulfill the requirements of the grasping force controller. In order to improve the force-tracking precision, the indirect adaptive algorithm was applied to estimate the parameters of the environment. The generalized momentum-based disturbance observer was applied to estimate the contact force from the torque sensor. Based on the sensors of the finger, an adaptive sliding mode friction compensation algorithm was utilized to improve the accuracy of the position control. The performances of the force-tracking impedance controller and position-based joint impedance control for the five-fingered anthropomorphic artificial hand are analyzed and compared in this paper. Furthermore, the performances of the force-tracking impedance controller with environmental parameters adaptive estimation and without environmental parameters estimation are analyzed and compared. Experimental results prove that accurate force-tracking and stable torque/force response under uncertain environments of unknown stiffness and position can be achieved with the proposed adaptive force-tracking impedance controller with friction compensation on five-finger artificial hand. [ABSTRACT FROM AUTHOR]

Path planning is one of the most important fields in robotics. Only a limited number of articles have proposed a practical way to solve the path-planning problem with moving obstacles. In this paper, a fuzzy path-planning method with two strategies is proposed to navigate a robot among unknown moving obstacles in complex environments. The static form of the environment is assumed to be known, but there is no prior knowledge about the dynamic obstacles. In this situation, an online and real-time approach is essential for avoiding collision. Also, the approach should be efficient in natural complex environments such as blood vessels. To examine the efficiency of the proposed algorithm, a drug delivery nanorobot moving in a complex environment (blood vessels) is supposed. The Monte Carlo simulation with random numbers is used to demonstrate the efficiency of the proposed approach, where the dynamic obstacles are assumed to appear in exponentially distributed random time intervals. [ABSTRACT FROM PUBLISHER]

Summary: In this paper, we propose a bioinspired path planning algorithm for finding a high-quality initial solution based on the pipeline of the Rapidly exploring Random Tree (RRT) method by modifying the sampling process. The modification mainly includes controlling the sampling space and using the probabilistic sampling with the two-dimensional Gaussian mixture model. Inspired by the tropism of plants, we use a Gaussian mixture model to imitate the tree's growth in nature. In a 2D environment, we can get an approximate moving point's probabilistic distribution, and the initial path can be found much quickly guided by the probabilistic heuristic. At the same time, only a small number of nodes are generated, which can reduce the memory usage. As a meta-algorithm, it can be applicable to other RRT methods and the performance of underlying algorithm is improved dramatically. We also prove that the probabilistic completeness and the asymptotic optimality depend on the original algorithm (other RRTs). We demonstrate the application of our algorithm in different simulated 2D environments. On these scenarios, our algorithm outperforms the RRT and the RRT* methods on finding the initial solution. When embedded into post-processing algorithms like the Informed RRT*, it also promotes the convergence speed and saves the memory usage. [ABSTRACT FROM AUTHOR]

This paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches. [ABSTRACT FROM AUTHOR]

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation. [ABSTRACT FROM AUTHOR]

We consider the conventional techniques of vision robot system calibration where camera parameters and robot hand–eye parameters are computed separately, i.e., first performing camera calibration and then carrying out hand–eye calibration based on the calibrated parameters of cameras. In this paper we propose a joint algorithm that combines the camera calibration and the hand–eye calibration together. The proposed algorithm gives the solutions of the cameras' parameters and the hand–eye parameters simultaneously by using nonlinear optimization. Both simulations and real experiments show the superiority of our algorithm. We also apply our algorithm in the real application of the robot-assisted surgical system, and very good results have been obtained. [ABSTRACT FROM PUBLISHER]

This paper presents an indirect adaptive fuzzy control scheme for a class of unknown multi-input multi-output (MIMO) nonlinear systems with external disturbances. Within this scheme, the dynamic fuzzy logic system (DFLS) is employed to identify the unknown nonlinear MIMO systems. The control law and parameter adaptation laws of DFLS are derived based on the Lyapunov synthesis approach. The control law is robustified in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions it guarantees the boundness of all signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. An extensive simulation on the tracking control of a two-link rigid robotic manipulator verifies the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates algorithms for enabling surgical slave robots to autonomously explore shape and stiffness of surgical fields. The paper addresses methods for estimating shape and impedance parameters of tissue and methods for autonomously exploring perceived impedance during tool interaction inside a tissue cleft. A hybrid force-motion controller and a cycloidal motion path are proposed to address shape exploration. An adaptive exploration algorithm for segmentation of surface features and a predictor-corrector algorithm for exploration of deep features are introduced based on discrete impedance estimates. These estimates are derived from localized excitation of tissue coupled with simultaneous force measurements. Shape estimation is validated in ex-vivo bovine tissue and attains surface estimation errors of less than 2.5 mm with force sensing resolutions achievable with current technologies in minimally invasive surgical robots. The effect of scan patterns on the accuracy of the shape estimate is demonstrated by comparing the shape estimate of a Cartesian raster scan with overlapping cycloid scan pattern. It is shown that the latter pattern filters the shape estimation bias due to frictional drag forces. Surface impedance exploration is validated to successfully segment compliant environments on flexible inorganic models. Simulations and experiments show that the adaptive search algorithm reduces overall time requirements relative to the complexity of the underlying structures. Finally, autonomous exploration of deep features is demonstrated in an inorganic model and ex-vivo bovine tissue. It is shown that estimates of least constraint based on singular value decomposition of locally estimated tissue stiffness can generate motion to accurately follow a tissue cleft with a predictor-corrector algorithm employing alternating steps of position and admittance control. We believe that these results demonstrate the potential of these algorithms for enabling “smart” surgical devices capable of autonomous execution of intraoperative surgical plans. [ABSTRACT FROM PUBLISHER]

This paper proposes a dynamic localization algorithm for a variable speed mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the time-of-flight of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high-speed mobile robot operations, instead of using two active beacons for localization, an active beacon and the predicted path are utilized to localize the mobile robot. This new approach resolves the high-localization error caused by the speed of mobile robot. The performance of this method was verified by comparing it to the conventional method through real-world experiments. [ABSTRACT FROM PUBLISHER]

This paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a canonical PID controller is discussed. In addition, the digital servo filters with feedforward are employed in the linear actuators as the lower layer controllers. Experimental results of a one-tenth scale FAST field model validate the effectiveness of the supervisory controller and the motion prediction algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a numerical algorithm to model the impact between articulated structures, or between an articulated structure and an object. The proposed algorithm, called impulse articulated-body algorithm (IABA), is based on the ABA but uses impulses and velocities rather than forces and accelerations. The algorithm also inherits the advantages of the ABA: versatility and low computational cost. The IABA provides a method to determine the impact impulse, without increasing the order of the algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a new algorithm for the control of a snake-like robot with passive joints and active wheels. Each segment has four autonomously driven and steered wheels. The algorithm approximates the ideal solution in which all wheels on a segment have the same centre of curvature with wheel speeds, providing cooperative redundancy. Each hitch point joining segments traverses the same path, which is determined by an operator, prescribing the path curvature and front hitch speed. The numerical algorithm developed in this paper is simulation tested against a previously derived analytical solution for a predetermined path. Further simulations are carried out to show the effects of changing curvature and front hitch speed on hitch path, wheel angles and wheel speeds for a one, two and three segment robot. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback–Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work. [ABSTRACT FROM AUTHOR]

This paper considers planning and control algorithms that enable a programmable sheet to realize different shapes by autonomous folding. Prior work on self-reconfiguring machines has considered modular systems in which independent units coordinate with their neighbors to realize a desired shape. A key limitation in these prior systems is the typically many operations to make and break connections with neighbors, which lead to brittle performance. We seek to mitigate these difficulties through the unique concept of self-folding origami with a universal fixed set of hinges. This approach exploits a single sheet composed of interconnected triangular sections. The sheet is able to fold into a set of predetermined shapes using embedded actuation.We describe the planning algorithms underlying these self-folding sheets, forming a new family of reconfigurable robots that fold themselves into origami by actuating edges to fold by desired angles at desired times. Given a flat sheet, the set of hinges, and a desired folded state for the sheet, the algorithms (1) plan a continuous folding motion into the desired state, (2) discretize this motion into a practicable sequence of phases, (3) overlay these patterns and factor the steps into a minimum set of groups, and (4) automatically plan the location of actuators and threads on the sheet for implementing the shape-formation control. [ABSTRACT FROM AUTHOR]

This paper presents a task-priority motion planning algorithm for underactuated robotic systems. The motion planning algorithm combines two features: the idea of the task-priority control of redundant manipulators and the endogenous configuration space approach. This combination results in the algorithm which solves the primary motion planning task simultaneously with one or more secondary tasks ordered in accordance with decreasing priorities. The performance of the task-priority motion planning algorithm has been illustrated with computer simulations of the motion planning problem for a container ship. [ABSTRACT FROM AUTHOR]

The technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms. [ABSTRACT FROM AUTHOR]

Reliability of any model-based failure detection and isolation (FDI) method depends on the amount of uncertainty in a system model. Recently, it has been shown that the use of joint torque sensing results in a simplified manipulator model that excludes hardly identifiable link dynamics and other nonlinearities such as friction, backlash, and flexibilities. In this paper, we show that the application of the simplified model in a fault detection algorithm increases reliability of fault monitoring system against modeling uncertainty. The proposed FDI filter is based on a smooth velocity observer of degree 2n where n stands for the number of manipulator joints. No velocity measurement and assumptions on smoothness of faults are used in the fault detection process. The paper focuses on actuator faults and investigates the effect of torque sensor noise on threshold selection. The FDI filter is further improved to become robust against an unknown bias in torque sensor reading. The effect of position sensor noise together with position sensor faults are also investigated. Simulation example on a 6-degrees of freedom manipulator is carried out to illustrate the performance of the proposed FDI method. [ABSTRACT FROM AUTHOR]

This paper describes a complete laser-based approach for tracking the pose of a robot in a dynamic environment. The main novelty of this approach is that the matching between consecutively acquired scans is achieved using their associated curvature-based representations. The proposed scan matching algorithm consists of three stages. Firstly, the whole raw laser data is segmented into groups of consecutive range readings using a distance-based criterion and the curvature function for each group is computed. Then, this set of curvature functions is matched to the set of curvature functions associated to the previously acquired laser scan. Finally, characteristic points of pairwise curvature functions are matched and used to correctly obtain the best local alignment between consecutive scans. A closed form solution is employed for computing the optimal transformation and minimizing the robot pose shift error without iterations. Thus, the system is outstanding in terms of accuracy and computation time. The implemented algorithm is evaluated and compared to three state of the art scan matching approaches. [ABSTRACT FROM AUTHOR]