Accurate load sensing is increasingly important in performing various tasks involved with contacting the environment. Most research has been focused on improving the hardware of a force/torque sensor. Torque sensors for a manipulator suffer from crosstalk, which cannot be compensated even by calibration. Thus they require precise machining and complicated structure to minimize crosstalk, which often results in cost increase. This research proposes a new calibration method composed of two steps. By the primary calibration, the torque sensor output can be related to the joint torques. The secondary calibration, which is based on the virtual load, is conducted to compensate crosstalk of the torque sensor. The virtual load is obtained from the measured joint torques and the manipulator configuration. Using the proposed calibration method, the external load acting on the end-effector can be sensed accurately even with relatively low-quality torque sensors. Experimental results showed that the error in load sensing was significantly reduced by the proposed calibration method.

Exploration is the fundamental task of guiding a robot autonomously during mapping so that it covers the entire environment with its sensors. In the frontier-based exploration, a robot visits the unknown regions, but the sufficient information on the obstacles was not exploited. In the topological exploration, the robot was forced to visit all the topological nodes, but it was inefficient and time-consuming. In this paper, an efficient exploration called a thinning-based topological exploration (TTE) is proposed. This scheme is based on the position probability of the end nodes of a topological map built in real time. The robot then updates the position probability of each end node sustaining its position at the current location using the range data. By analyzing this position probability, the robot can determine whether or not it needs to visit the specific end node to examine the environment around this node. Various experiments show that the proposed TTE algorithm can perform exploration more accurately than the frontier-based exploration approach and more efficiently than the other topological exploration schemes, because in most cases, exploration for the entire environment can be completed without directly visiting everywhere

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Many studies have been conducted to improve performance of MCL. Although MCL is capable of estimating the robot pose when the initial pose of a robot is not given, it takes much time for convergence because a large number of random samples are required, especially for the large-scale environment. For practical implementation of MCL, therefore, it is desirable to reduce the number of samples without affecting the localization performance. This paper presents a novel approach to reduce the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information is extracted in real time through the thinning algorithm from the range data of a laser scanner. The topological map is first created from the given grid map of the environment. The robot scans the local environment and generates a local topological map. The robot then navigates along this local topological edge, which coincides with the global topological map obtained off-line from the given global grid map. By constraining the robot's motion on this local edge, random samples are drawn only around the neighborhood of the topological edge rather than over the entire free space. Hence the sample size required for MCL can be drastically reduced, thereby reducing computational time for the MCL process. A series of experiments based on this proposed MCL/TI show that the number of samples can be reduced considerably, and thus the time required for pose estimation can be substantially decreased