This paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.

This paper deals with a generic modeling and vision-based control approach for a broad class of parallel mechanisms. First, a generic architecture representing several families is proposed. Second, inspired by the geometry of lines, a generic differential inverse kinematic model according to the proposed generic structure is introduced. Finally, based on the image projection of cylindrical legs, a kinematic vision-based control using legs observation is presented. The approach is illustrated and validated on the Gough–Stewart and Par4 parallel robots.

In this paper, we propose a novel type of serial robot with minimal actuation. The robot is a serial rigid structure consisting of multiple links connected by passive joints and of movable actuators. The novelty of this robot is that the actuators travel over the links to a given joint and adjust the relative angle between the two adjacent links. The joints passively preserve their angles until one of the actuators moves them again. This actuation can be applied to any serial robot with two or more links. This unique configuration enables the robot to undergo the same wide range of motions typically associated with hyper-redundant robots but with much fewer actuators. The robot is modular and its size and geometry can be easily changed. We describe the robot's mechanical design and kinematics in detail and demonstrate its capabilities for obstacle avoidance with some simulated examples. In addition, we show how an experimental robot fitted with a single mobile actuator can maneuver through a confined space to reach its target.

This paper proposes a new antagonistic tendon-driven joint (TDJ) that exhibits higher stiffness and larger travel range than conventional types of TDJs. A detailed mathematical analysis of the stiffness of the proposed TDJ is conducted and compared to other TDJs. The effect of the tendon length is taken into consideration to establish a more precise and realistic stiffness model of the proposed TDJ. Thereafter, two hardware prototypes of the proposed TDJ design, developed in the form of a packaged modular structure that integrates two TDJs, are introduced. Using these prototypes, the stiffness characteristics of the proposed TDJs are verified through experimentation. Additionally, experimental results on the stiffness behavior during the mimicked needle insertion tasks are provided. Results show that the proposed TDJs present much higher stiffness than conventional ones and thus give a potential benefit to precision manipulation.

Autonomous multi-agent systems show promise in countless applications, but can be hindered in environments where inter-agent communication is limited. In such cases, this paper considers a scenario where agents communicate intermittently through a cloud server. We derive a graph transformation mapping the kernel of a graph's Laplacian to a desired configuration vector while retaining graph topology characteristics. The transformation facilitates derivation of a self-triggered controller driving agents to prescribed configurations while regulating instances of inter-agent communication. Experimental validation of the theoretical results shows the self-triggered approach drives agents to a desired configuration using fewer control updates than traditional periodic implementations.

A robot should be able to estimate an accurate and dense 3D model of its environment (a map), along with its pose relative to it, all of it in real time, in order to be able to navigate autonomously without collisions.

As the robot moves from its starting position and the estimated map grows, the computational and memory footprint of a dense 3D map increases and might exceed the robot capabilities in a short time. However, a global map is still needed to maintain its consistency and plan for distant goals, possibly out of the robot field of view.

In this work, we address such problem by proposing a real-time stereo mapping pipeline, feasible for standard CPUs, which is locally dense and globally sparse and accurate. Our algorithm is based on a graph relating poses and salient visual points, in order to maintain a long-term accuracy with a small cost. Within such framework, we propose an efficient dense fusion of several stereo depths in the locality of the current robot pose.

We evaluate the performance and the accuracy of our algorithm in the public datasets of Tsukuba and KITTI, and demonstrate that it outperforms single-view stereo depth. We release the code as open-source, in order to facilitate the system use and comparisons.

This paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.

In this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method.

This paper describes a new robotic manipulator with three fingers based on an origami twisted tower design. The design specifications, kinematic description, and results from the stiffness and durability tests for the selected origami design are presented. The robotic arm is made of a 10-layer twisted tower, actuated by four cables with pulleys driven by servo motors. Each finger is made of a smaller 11-layer tower and uses a single cable directly attached to a servo motor. The current hardware setup supports vision-based autonomous control and internet-based remote control in real time. For preliminary evaluation of the robot's object manipulation capabilities, arbitrary objects with varying weights, sizes, and shapes (i.e., a shuttlecock, an egg shell, a paper cub, and a cubic block) were selected and the rate of successful grasping and lifting for each object was measured. In addition, an experiment comparing a rigid gripper and the new origami-based manipulator revealed that the origami structure in the fingers absorbs the excessive force applied to the object through force distribution and structural deformation, demonstrating its potential applications for effective manipulation of fragile objects.

This study presents a new kinetostatic model for humanoid robots (HRs). Screw theory, together with Assur virtual chains and Davies' method, provides the required tools for the proposal of both the kinematic and static parts of the kinetostatic model. Our kinetostatic model is able to estimate the forces and couples generated at the axes of each joint of the robot, as well as one unknown contact condition between the robot and the environment around it. The proposed model is also very versatile and free of fixed coordinates and, therefore, it allows for an estimate of a great amount of information on the HR. Some results, obtained from computer simulation, are presented to validate the versatility of the proposed technique.

In this paper, a new calibration method for open-chain robotic arms is developed. By incorporating both prior parameter information and artifact measurement data, and by taking recourse to Bayesian inference methods, not only are the robot kinematic parameters updated but also confidence bounds are computed for all measurement data. In other words, for future measurement data not only the most likely end-effector configuration is estimated but also the uncertainty represented as 95% confidence bounds of that pose is computed. To validate the proposed calibration method, a three degree-of-freedom robotic arm was designed, constructed, and calibrated using both typical regression methods and the proposed calibration method. The results of an extensive set of experiments are presented to gauge the accuracy and utility of the proposed calibration method.

In this paper, the kinematic accuracy problem caused by geometric errors of a 2(3HUS+S) parallel manipulator is described. The kinematic equation of the manipulator is obtained by establishing a D–H (Denavit–Hartenberg) coordinate system. A D–H transformation matrix is used as the error-modeling tool, and the kinematic error model of the manipulator integrating manufacturing and assembly errors is established based on the perturbation theory. The iterative Levenberg–Marquardt algorithm is used to identify the geometric errors in the error model. According to the experimentally measured attitudes, the kinematic calibration process is simulated using MATLAB software. The simulation and experiment results show that the attitude errors of the moving platforms after calibration are reduced compared with before the calibration, and the kinematic accuracy of the manipulator is significantly improved. The correctness and effectiveness of the error model and the kinematic calibration method of the 2(3HUS+S) parallel manipulator for simulation of hip joint motion are verified.

Existing measurement equipments easily determine position with high precision. However, they evaluate orientation with low precision. It is necessary to minimize the effect of measurement error on identification accuracy. In this study, a method for kinematic calibration based on the product of exponentials (POE) is presented to improve the absolute positioning accuracy of a sliding manipulator. An error model with uniform and generic modeling rules is established in which the tool frame is selected as the reference frame. Furthermore, the redundant parameters of the error model are removed. Subsequently, the actual kinematic parameters are identified by using the least square method. Finally, the process of the improved method is discussed. Kinematic calibration simulations of a sliding manipulator are implemented. The results indicate that the proposed method significantly improves the precision of the sliding manipulator. The improved POE kinematic calibration method offers convenience, efficiency, and high precision. The proposed method can be applied to all types of serial robots with n-DOF

If the joint clearances of the joints of a manipulator are considered, an unconstrained motion of the end-effector can be computed. This is true for all poses of the manipulator, even with all actuators locked.

This paper presents how this unconstrained motion can be determined for a planar 3-RPR manipulator. The singularities are then studied. It is shown that when clearances are considered, the singularity curves normally found in the workspace of such a manipulator become singular zones. These zones can be significant and greatly reduce the usable workspace of a manipulator. Since a prescribed configuration that would not, in theory, corresponds to a singular pose can become singular due to the unconstrained motion, the results of this paper are relevant to manipulator design and trajectory planning.

Assist-as-needed control is underlain by the aim of replacing skillful therapists with rehabilitation robots. The objective of this research was to introduce a smart assist-as-needed control system for the elderly or partially paralyzed individuals. The main function of the proposed system is to assist the patients just in the required sub phases of the motion. To ensure that a smart and compliant system is developed, the target admittance gains of the controller was adapted according to the concept of energy The admittance gains were modified so that an exoskeleton reduces interaction energy in cases wherein users have sufficient strength for task execution and maximizes the interaction energy in the required subphases. The results of simulations and an experimental investigation on a novel exoskeleton showed that the proposed adaptive admittance control improves performance to a level substantially higher than that achieved with constant impedance control.

This study focuses on a door-opening mobile manipulator operating in four phases (reaching the door, grasping the door handle, turning the door handle, and pulling the door). We use force/torque feedback-based control, achieving compliance of the mobile base when it comes into contact with the handle. A method is proposed for estimating the unknown force-related constraints from manipulator joint position measurements. A robust adaptive control strategy is developed for tracking the planned trajectory to open the door. Finally, a mobile manipulator opens a real door with a locked latch and unknown force-related constraints, demonstrating the validity of the proposed approach.

The constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.

This paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach.

The quadrotor aerial robot is a complex system and its dynamics involve nonlinearity, uncertainty, and coupling. In this paper, an adaptive backstepping sliding mode control (ABSMC) is presented for stabilizing, tracking, and position control of a quadrotor aerial robot subjected to external disturbances. The developed control structure integrates a backstepping and a sliding mode control approach. A sliding surface is introduced in a Lyapunov function of backstepping design in order to further improve robustness of the system. To attenuate a chattering problem, a saturation function is used to replace a discontinuous sign function. Moreover, to avoid a necessity for knowledge of a bound of external disturbance, an online adaptation law is derived. Particle swarm optimization (PSO) algorithm has been adopted to find parameters of the controller. Simulations using a dynamic model of a six degrees of freedom (DOF) quadrotor aerial robot show the effectiveness of the approach in performing stabilization and position control even in the presence of external disturbances.

This paper presents and compares a set of calibration strategies useful to calibrate vision-based robotised work-cells for micromanipulation and microassembly. To grasp and release microparts precisely, robot calibration, camera calibration and robot-camera registration are needed. Conventional calibration methods are very onerous at the microscale, therefore, two alternative unconventional procedures, called virtual grid calibration and hybrid calibration, are developed for work-cells with high-performance robots, minimising necessary instrumentation. Moreover, an effective calibration of the robot end-effector is designed to compensate for misalignment and orientation errors with respect to the vertical rotational axis. This paper describes the calibration methods and their implementation, the results and the improvements achieved. A detailed comparison between the hybrid and the virtual grid calibrations is provided, demonstrating the higher performance of the latter strategy.