This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.

This paper reports the design of sliding-mode control laws for controlling multiple small-sized autonomous helicopters in arbitrary formations. Two control schemes, which are required for defining arbitrary three-dimensional formation meshes, are discussed. In the presented leader–follower formation control schemes, each helicopter only needs to receive motion information from at most two neighboring helicopters. A nonlinear six-degree-of-freedom dynamic model has been used for each helicopter. Four control inputs, the main and the tail rotor thrusts, and the roll and pitch moments, are assumed. Parameter uncertainty in the dynamic model and wind disturbance are considered in designing the controllers. The effectiveness and robustness of these control laws in the presence of parameter uncertainty in the dynamic model and wind disturbances are demonstrated by computer simulations.

This paper suggests a generalized approach to the mathematical modeling of biped locomotion systems (humans or humanoid robots) with special attention paid to impact and contact dynamics. Instead of the usual inductive approach that starts from the analysis of different situations of real motion (walking, running, jumping, climbing the stairs, etc.) and tries to make a generalization, a deductive approach is pursued, whereby an entirely general problem is considered. Impact dynamics is modeled applying the linear complementarity problem (LCP) formulation. General methodology is explained and demonstrated via the synthesis of a spatial biped model. The validity of the modeling approach is proved by experimental measurements on a human subject in the laboratory conditions. Plenty of graphic presentations illustrating experimental results, as well as the results of the corresponding simulation tests are shown.

In order to widen the potentialities of manipulation of the Laboratoire de Mécanique des solides (LMS) mechanical hand, we developed a new planning approach based on the use of a specific exoskeleton. This one has kinematics architecture and dimensions identical to the mechanical hand. This feature allows us to obtain manipulation trajectories for the mechanical hand, very easily and very quickly, by using the exoskeleton, without complex calibration. Manipulation's trajectories are replayed offline with an autonomous control, and, consequently, the exoskeleton is not used with any feedback strategy for telemanipulation. This paper presents the characteristics of this exoskeleton and the graphic interface that we developed. This one uses a method to determine the object's evolution during the manipulation with the exoskeleton, without using exteroceptive sensors. This new approach was tested for standard trajectories by simulation on a Computer-aided design (CAD) robotics system and by using the mechanical hand. Thus, we validate the use concept of an isomorphic exoskeleton to mechanical hand for manipulation planning with the LMS mechanical hand.

Recent advances in simultaneous localization and mapping permit robots to autonomously explore enclosed environments and, subsequently, navigate to selected positions within them. But, for many tasks, it is more useful to immediately navigate to goals in unexplored environments, without a map. This is possible if a human director can describe the ideal route to the robot using grounded symbols that both parties can perceive directly.

In this paper, a mobile robot is autonomously navigated to many locations in a cluttered laboratory environment by a variety of routes. A series of topological navigation instructions are provided in advance by the director, in a form that can be expressed verbally and translates easily to software representation. The instructions are based on the perception of spatial affordances available to the robot, namely nearby junctions and edges in a pruned Generalized Voronoi Diagram. The operator can generate the instructions by viewing or imagining the environment without any measurements. Only three to five instructions are needed to navigate anywhere in our laboratory. The instructions contain only topology. No spatial measurements or environmental data such as landmarks are provided to the robot.

The state-of-the-art FastSLAM algorithm has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to conduct loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), a rank-based resampling adopting genetic algorithms are analyzed using computer simulations. Several performance measures such as the effective sample size, the number of distinct particles, estimation errors, and complexity are used for the thorough analysis of the resampling algorithms. Moreover, a new compensation technique is proposed instead of resampling to resolve the particle depletion problem in FastSLAM. In estimation errors, the compensation technique outperformed other resampling algorithms though its run-time was longer than those of others. The most appropriate time to instigate compensation to reduce the run-time was also analyzed with the diminishing number of particles.

This investigation describes the mechanical configuration and control environment for a novel cart-seesaw system. This mechanism is called a super articulated mechanical system (SAMS). The system comprises a cart that slides on the pneumatic rodless cylinder. The rodless cylinder is double-acting with the carrier bracket, on which a cart is a pinion mechanism for the tracks. The cart-seesaw system brings the cart from any initial position to a desired position on the seesaw by applying an appropriate force to the cart and thus adjusting the angle of the seesaw. The position of a cart denotes the first degree of freedom, which is activated by a pneumatic proportional valve, and the angle of the seesaw indicates the second degree of freedom that is not actuated. Consequently, the proposed new pneumatic cart-seesaw system is straightforward to construct and direct to operate in different scenarios of performance. A state feedback controller is applied for stabilization of the equilibrium point of the system. Moreover, this study adds a supervisory controller that takes control action in extreme situations. Test results reveal excellent properties in control performance. The proposed product can be extensively applied in SAMS and pneumatic control for robotics control laboratory.

In this paper, a novel algorithm is formulated and implemented for optimum path planning of parallel manipulators. A multi-objective optimisation problem has been formulated for an efficient numerical solution procedure through kinematic and dynamic features of manipulator operation. Computational economy has been obtained by properly using a genetic algorithm to search an optimal solution for path spline-functions. Numerical characteristics of the numerical solving procedure have been outlined through a numerical example applied to Cassino Parallel Manipulator (CaPaMan) both for path planning and design purposes.

Global localization of mobile robots has been well studied using the extended Kalman filter (EKF) method. This paper presents a fuzzy extended information filtering (FEIF) approach to improving global localization of an indoor autonomous mobile robot with ultrasonic and laser scanning measurements. A real-time FEIF algorithm is proposed to improve accuracy of static global pose estimation via multiple ultrasonic data. By fusing odometric, ultrasonic, and laser scanning data, a real-time FEIF-based pose tracking algorithm is developed to improve accuracy of the robot's continuous poses. Several experimental results are performed to confirm the efficacy of the proposed methods.