Search results

The purpose of this paper is to present a new vision-based control method, which enables delta-type parallel robots to track and manipulate objects moving in arbitrary trajectories. This constitutes an enhanced variant of the linear camera model-camera space manipulation (LCM-CSM).

After obtaining the LCM-CSM view parameters, a moving target’s position and its velocity are estimated in camera space using Kalman filter. The robot is then commanded to reach the target. The proposed control strategy has been experimentally validated using a PARALLIX LKF-2040, an academic delta-type parallel platform and seven different target trajectories for which the positioning errors were recorded.

For objects that moved manually along a sawtooth, zigzag or increasing spiral trajectory with changing velocities, a maximum positioning error of 4.31 mm was found, whereas objects that moved on a conveyor belt at constant velocity ranging from 7 to 12 cm/s, average errors between 2.2-2.75 mm were obtained. For static objects, an average error of 1.48 mm was found. Without vision-based control, the experimental platform used has a static positioning accuracy of 3.17 mm.

The LCM-CSM method has a low computational cost and does not require calibration or computation of Jacobians. The new variant of LCM-CSM takes advantage of aforementioned characteristics and applies them to vision-based control of parallel robots interacting with moving objects.

A new variant of the LCM-CSM method, traditionally used only for static positioning of a robot’s end-effector, was applied to parallel robots enabling the manipulation of objects moving along unknown trajectories.

Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes.

The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge.

The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene.

This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real-life scenarios, such as EuRoC and Amazon Picking Challenge.

The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

The purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.

Cooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.

The results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.

A simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

The basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

Aims to review the Clawar 2005 conference on climbing and walking robots.

Selects key papers from the conference and presents a brief outline of the research undertaken and the conclusions reached.

Speakers told of the latest developments in humanoid robots; space applications; personal assistance robots; and NDT, security and surveillance. Sessions also covered innovations in sensing and sensor fusion; climbing, navigation and path planning, gait generation, manipulation; hopping and legged robots; flexible manipulators; and control.

Outlines trends in the development of climbing and walking robots.

New technologies, including artificial intelligence (AI), have helped us begin to take our first steps off Earth and into outer space. But conflicts inevitably will arise and, in the absence of settled governance, may be resolved by force, as is typical for new frontiers. But the terrestrial assumptions behind the ethics of war will need to be rethought when the context radically changes, and both the environment of space and the advent of robotic warfighters with superhuman capabilities will constitute such a radical change. This essay examines how new autonomous technologies, especially dual-use technologies, and the challenges to human existence in space will force us to rethink the ethics of war, both from space to Earth, and in space itself.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

The purpose of this paper is to show how a swarm of robots can cooperate to achieve a common task, in a totally distributed and autonomous way, by exploiting powerful clues contained in some devices that are distributed in the environment. This system exploits a coordination mechanism that is twofold, using radio frequency identification (RFID) tags for spatial coordination, and wireless robot‐to‐robot communication for the temporal and semantic synchronization.

Progress in the pervasive computing field has led to the distribution of knowledge and computational power in the environment, rather than condensing it in a single, powerful entity. This vision of ambient intelligence is supported by the interchange of information between physically sparse agents cooperating to achieve a common goal. An emerging method for this kind of collaboration considers the agents as insects in a swarm, having the possibility of communicating directly or indirectly with each other. The goal is to fulfill a common task, showing that a collaborative behavior can be useful in the real world. The paper focuses on a technique for the coordination of swarm‐robots with low capabilities, driven by instructions learned from RFID tags used as distributed pervasive memories. These robots exploit ubiquitous computing to regroup in a synchronization area, make a formation in space, coordinate with team‐mates in the same zone, and finally complete a cooperative task. The algorithm is validated through a simulation environment, showing its applicability and performance, before the real implementation on Roomba‐like robots.

The goal of the research is to prove the feasibility of such a novel approach. It is observed that a swarm of robots can achieve a good degree of autonomous cooperation without a central infrastructure or global network, carrying out a goal in a fair time.

The value is given by the benefits of splitting the synchronization semantics into two levels: space, by exploiting RFID landmarks; and time, by exploiting wireless short‐range communication. RFID tags are used to distribute computational power and actively interact with the surrounding areas, allowing to learn and modify the state of the environment. Robot‐to‐robot communication, instead, is used for providing timing and semantic information. In the proposal, this augmented environment is used to allow a good level of coordination among robots, both in time and space, with the aim of building a cooperative system.

The purpose of this paper is to develop a set of ground experiment system to verify the basic functions of space effector and the capturing reliability of space end-effector for the free-floating target payload in the three-dimensional space. The development of ground experiment system for space end-effector is essential and significant, because it costs too much to launch a space robot or other spacecraft and carry out operation tasks in space. Owing to the negligible gravity in space, which is different from that in the ground environment, ground experiment system for space end-effector should have the capability of verifying the basic functions of space effector and the reliability of space end-effector in capturing the free-floating target payload in space.

The ground experiment system for space end-effector mainly adopts the hybrid simulation method, which includes the real hardware experiment and software simulation. To emulate the micro-gravity environment, the contact dynamics simulator is applied to emulating the motion state of the free-floating target payload, while the admittance control is used to realize the “soft” capturing of space end-effector to simulate the real situation in space.

With the gravity compensation, the influence of gravity is almost eliminated and the results meet the requirements of the experiment. In the ground experiment, the admittance control is effective and the actual motion state of space end-effector capturing the target in space can be simulated. The experiment results show that space end-effector can capture the free-floating target payload successfully and hopefully have the ability to capture a free-floating target in space.

The system can verify space end-effector capturing the free-floating target payload in three-dimensional space and imitate the motion of space end-effector capturing the free-floating target in space. The system can also be modified and improved for application in the verification of space robot capturing and docking the target, which is valuable for the ground verification of space applications.

The aim of this paper is to present a novel methodology which incorporates Camshift, Kalman filter (KFs) and adaptive multi-space transformation (AMT) for a human-robot interface, which perfects human intelligence and teleoperation.

In the proposed method, an inertial measurement unit is used to measure the orientation of the human hand, and a Camshift algorithm is used to track the human hand using a three-dimensional camera. Although the location and the orientation of the human can be obtained from the two sensors, the measurement error increases over time due to the noise of the devices and the tracking errors. KFs are used to estimate the location and the orientation of the human hand. Moreover, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. An AMT method is proposed to assist the operator to improve accuracy and reliability in determining the pose of the robot.

The experimental results show that this method would not hinder most natural human-limb motion and allows the operator to concentrate on his/her own task. Compared with the non-contacting marker-less method (Kofman et al., 2007), this method proves more accurate and stable.

The human-robot interface system was experimentally verified in a laboratory environment, and the results indicate that such a system can complete high-precision manipulation efficiently.

The purpose of this paper is to propose an efficient method, called kinodynamic velocity obstacle (KidVO), for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs).

The suggested method improves generalized velocity obstacle (GVO) approach by a systematic selection of proper time horizon. Selection procedure of the time horizon is based on kinematical and dynamical restrictions of the robot. Toward this aim, an omnimobile robot with a general geometry is taken into account, and the admissible velocity and acceleration cones reflecting KDCs are derived, respectively. To prove the advantages of the suggested planning method, its performance is compared with GVOs, the so-called Hamilton-Jacobi-Bellman equation and the rapidly exploring random tree.

The obtained results of the presented scenarios which contain both computer and real-world experiments for complicated crowded environments indicate the merits of the suggested methodology in terms of its near-optimal behavior, successful obstacle avoidance both in static and dynamic environments and reaching to the goal pose.

This paper proposes a novel method for online motion planning of omnimobile robots in dynamic environments while considering the real capabilities of the robot.