This paper proposes a novel design method of a shared controller for telerobot systems. A shared controller can enlarge a reflected force by combining force reflection and compliance control. However, the maximum boundary of the force reflection gain guaranteeing the stability greatly depends upon characteristics of the elements in the system such as; a master arm which is combined with the human operator's hand, the environments where the slave arm contacts and the compliance controller. In normal practice, it is therefore, very difficult to determine such a maximum boundary of the gain. To overcome this difficulty, the paper proposes a force reflection gain-selecting algorithm based on neural network and fuzzy logic features. The method estimates characteristic of the master arm and the environments by using neural networks, and then, determines the force reflection gain from the estimated characteristics by using fuzzy logic. The algorithm can work in an on-line manner, and can be easily applied to any telerobot system because it requires no a priori knowledge on the system. The effectiveness of the proposed control scheme is verified through a series of experiments using a laboratory-made telerobot system.

Robotic surface planetary exploration is a challenging endeavor, with critical safety requirements and severe communication constraints. Autonomous navigation is one of the most crucial and yet risky aspects of these operations. Therefore, a certain level of local autonomy for onboard robots is an essential feature, so that they can make their own decisions independently of ground control, reducing operational costs and maximizing the scientific return of the mission. In addition, existing tools to support research in this domain are usually proprietary to space agencies, and out of reach of most researchers. This paper presents a framework developed to support research in this field, a modular onboard software architecture design and a series of algorithms that implement a visual-based autonomous navigation approach for robotic space exploration. It allows analysis of algorithms' performance and functional validation of approaches and autonomy strategies, data monitoring and the creation of simulation models to replicate the vehicle, sensors, terrain and operational conditions. The framework and algorithms are partly supported by open-source packages and tools. A set of experiments and field testing with a physical robot and hardware are described as well, detailing results and algorithms' processing time, which experience an incremented of one order of magnitude when executed in space-certified like hardware, with constrained resources, in comparison to using general purpose hardware.

A robot working in a hostile environment should be equipped with the necessary risk-avoidance intelligence to survive and accomplish an assigned mission. Covert robotics is a relatively new field in which covert navigation abilities are developed for robots to carry out a mission with minimal potential observation from known or unknown vantage points, with occluding obstacles as the only means of maintaining covertness. The ability to ‘hide’ in order to minimize the risk of being detected is the focus of this paper. A number of strategies are presented that deal with various assumptions about the environment and the hostile observers' locations. The hiding strategies presented for different cases ensure choosing a proper hiding point while taking into account the risk of the chosen path to it.

In this work, we discuss the design, construction, and testing of a cable-controlled motion simulator for a Virtual Reality (VR) hang gliding environment. The system comprises a cable-controlled motion simulator, a joystick, a Lego™ direction sensor, and a VR environment. The motion simulator and joystick are built out of motors, pulleys, cables, and aluminum beams. The VR environment and motion simulator interact haptically with each other to give a realistic feel to the operator. A dynamic analysis is performed on the simulator to show the effect of gravity and the directional motion on the operator. A series of experiments are then performed to test the effectiveness of the cable-controlled simulator, and the results were very encouraging despite minor glitches with high-speed maneuvers.

This paper presents a new recursive algorithm of robot dynamics based on the Kane's dynamic equations and Newton-Euler formulations. Differing from Kane's work, the algorithm is general-purpose and can be easily realized on computers. It is suited not only for robots with all rotary joints but also for robots with some prismatic joints. Formulations of the algorithm keep the recurrence characteristics of the Newton-Euler formulations, but possess stronger physical significance. Unlike the conventional algorithms, such as the Lagrange and Newton-Euler algorithm, etc., the algorithm can be used to deal with dynamics of robots containing closed chains without cutting the closed chains open. In addition, this paper makes a comparison between the algorithm and those conventional algorithms from the number of multiplications and additions.

In this paper, we are introducing a system which is able to recognize polyhedral objects in an indoor environment. Our system is intended to be implemented on autonomous mobile platforms in order to enable the localization or research of a precise item. The algorithm is based on the use of geometric quasi-invariants associated to every object. These geometric quasi-invariants correspond to the ratio of the lengths as well as the angle formed by the pair of segments which are in relationship and which are constituting the object. We present some experimental results gained on one of our platforms in our laboratory.

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce the human superior experience as part of input. In this paper, we propose a framework for robot learning based on demonstration and supervision. Superior experience supplied by teleoperation is introduced to deal with unknown environment constrains and correct the demonstration for next execution. DMPs model with integral barrier Lyapunov function is used to deal with the constrains in robot learning. Additionally, a radial basis function neural network based controller is developed for teleoperation and the robot to track the generated motions. Then, we prove convergence of the generated path and controller. Finally, we deploy the novel framework with two touch robots to certify its effectiveness.

In this paper a multirobot system consisting of small size ball-shaped mobile underwater robots is introduced. Robots form a cooperative society operating together for a common goal. This is made possible by inter-member communication and control architecture allowing cooperation. The test environment is a closed aquatic process containing tanks, pipes, and a jet pump. The task considered is cleaning of biologically contaminated spots in the process. Detailed hardware structure of a robot-member as well as the control architecture are introduced. Behaviour of the cooperative system is demonstrated in a test environment where contamination caused by biological algae growth is emulated by infrared panels behaving like a living biomass.

Mobile robotics is a very attractive research field. There are two main reasons for this. The first is the high number of applications and sectors where these machines could be introduced in the future: transport, agriculture, military, aerospace, etc. The second reason is that mobile robots are excellent test-beds to develop and integrate the results of many other research areas: control, computer vision, electronics, mechanics, artificial intelligence, etc. The third IFAC Symposium on Intelligent Autonomous Vehicles was an opportunity to present some of the latest developments in the area. Authors presented not only new methods and technologies to solve the classical problems related to intelligent autonomous vehicles, which include path planning, localization, environment modeling, path following, etc., but also new approaches to their design, such as new architectures, new navigation procedures, self-learning systems, etc. In addition, many different applications and vehicles were considered: from autonomous ocean vehicles to planetary rovers. This special issue of Robotica comprises some revised and updated selected papers of the Symposium. The variety of selected papers includes surveys, applications and design and development aspects of intelligent autonomous vehicles.

This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.

Heavy-duty hexapod robots are well-suited for physical transportation, disaster relief, and resource exploration. The immense locomotion capabilities conferred by the six appendages of these systems enable traversal over unstructured and challenging terrain. However, tipping can be a serious concern when moving with a tripod gait in these challenging environments, which may cause irreversible consequences such as compromised movement control and potential damage. In this paper, we focus on heavy-duty hexapod robot sideline tipping judgment and recovery during tripod gait motion, and a novel sideline tipping judgment and recovery method is proposed by adjusting an optimal swinging leg to the stance state. Considering the locomotion environments, motion mode, and tipping analysis, the robot’s stability margin is quantified, and the tipping event is evaluated by the Force Angle Stability Measure (FASM). The recovery method is initiated upon detecting that the robot is tipping, which involves the selection of an adjustment leg and the determination of an optimal foothold. Since the FASM is based on the foot force and robot center of gravity (CoG), the stability margin quantification expression is reformulated to the constraint form of quadratic programming (QP). Furthermore, a foot force distribution method, integrating stability margin considerations into the QP model, has been devised to ensure post-adjustment stability of the landing leg. Experiments on tipping judgment and recovery demonstrate the effectiveness of the proposed approaches on tipping judgment and recovery.

An online pattern recognition method of lower limb movements is proposed based on the personalized surface electromyography (sEMG) signals, and the corresponding experimental researches are performed in the rehabilitation training. Further, a wireless wearable acquisition instrument is used. Based on this instrument, a host computer for the personal online recognition and real-time control of rehabilitation training is developed. Three time-domain features and two features in the nonlinear dynamics are selected as the joint set of the characteristic values for the sEMG signals. Then a particle swarm optimization (PSO) algorithm is used to optimize the feature channels, and a k-nearest neighbor (KNN) algorithm and the extreme learning machine (ELM) algorithm are combined to classify and recognize individual sample data. Based on the multi-pose lower limb rehabilitation robot, the real-time motion recognition and the corresponding rehabilitation training are carried out by using the online personalized classifier. The experimental results of eight subjects indicate that it takes only 6 min to build an online personalized classifier for the four types of the lower limb movements. The recognition between switches of different rehabilitation training movements is timely and accurate, with an average recognition accuracy of more than 95%. These results demonstrate that this system has a strong practicability.

Robot intensiveness in the manufacturing industry is growing rapidly, but the construction industry has been slow to capitalise on the array of robotic technology now available. An attempt is made to indentify various robot ensembles that may prove economically viable in building construction, and to configure these in such a way that particular characteristics of robot work methods are used to maximum advantage within the constraints of the construction environment.

In disassembly tasks, due to the large variety of objects and the different positions and orientations in which they appear, the disassembly trajectories supplied on-line by a human operator or an automatic recognition system can contain large errors. The classical compliant control methods turn out to be insufficient to eliminate sticking which is due to these errors. This paper presents a compliant control method for disassembly of non-elastic parts in non-elastic environments which adopts the trajectories according to realised motion. In case of sticking a new direction of motion is searched for until the manipulated part is set into motion.

It is shown that if one comes back to the formulation of the Hamilton's variational principle, it is then possible to obtain new viewpoints on the tracking control of robot manipulators. First, the Lagrange multiplier associated to the sliding surface can be interpretated in terms of control effort and/or forces of reaction of the mechanical system. Secondly, one can use the Taylor expansion of the mechanical Lagrangian, combined with a neighbour- ing Hamilton's principle, to obtain control schemes via sliding surfaces. Thirdly, a perturbation approach combined with the neighbouring Hamilton's principle provides results on the robustness of the control.

Position estimation is a key issue for an ALV Autonomous Land Vehicle) in navigating a mountainous area. The unevenness of the terrain makes mechanical velocity sensors inaccurate (due to wheel slippage), and the lack of appropriate landmarks complicates the problem. In this paper, we present a solution method using features of the skyline. The skyline from the vision system is assumed given, and compared with a computer map, called the CAD-MAP. The algorithm is composed of: a) Identification of the peak points in the camera skyline, b) Computing the ALV position for the identified peak points, and c) Searching for the corresponding peak point in the CAD-MAP. Heuristics for computational efficiency and solution accuracy are also included in the algorithm. To test the validity and effectiveness of the algorithm, numerous simulations were performed and analyzed.