Accurate force reflection method for a multi-d.o.f. haptic interface using instantaneous restriction space without a force sensor in an unstructured environment

This paper proposes an accurate force reflection method for a multi-d.o.f. haptic interface without a force sensor. Sensorless force reflection is possible using position–position (p–p) architecture. However, the conventional p–p architecture in the literature has limitations representing constraint space when it is applied to a multi-d.o.f. haptic interface in that it gives an inaccurate force direction. This paper demonstrates the limitation of the conventional p–p architecture through an example and proposes a novel force reflection method using the instantaneous restriction space (IRS) concept. The IRS can be calculated using the Jacobian and joint angle error of a slave manipulator. Since the proposed method has the form of an impedance two-port architecture in the sense of data flow, it can be easily combined with previous well-known results of two-port haptic display frameworks. The proposed method is especially useful when the slave manipulator collides with unexpected obstacles during motion, even though the slave does not have a force sensor. The performance of the proposed method is evaluated through experiments.11Nsciescopu

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments. (C) Koninklijke Brill NV, Leiden, 2011X1122sciescopu

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.X111920sciescopu