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This paper aims to present object or feature segmentation from an ordered 3D point cloud range data obtained from a laser scanner for the purpose of robot navigation.

Rotating multi-beam laser scanners provide ordered 3D range data. Differences between consecutive ranges in radial direction are used to compute a novel measure of terrain unevenness at each data point. Computed over a complete rotation, an unevenness field is formed surrounding the scanner. A part of this field staying below a threshold is recognized as ground and removed. Remaining non-ground points are segmented into objects by region growing with points whose unevenness lies within pre-specified limiting values.

The proposed unevenness attribute is simple and efficient for segmenting distinct objects or features. The fineness of surface features can be regulated by adjusting the threshold value of difference in unevenness between neighbouring points that triggers an onset of new segments.

The angles between neighbouring laser range data are assumed to be known.

Segmented objects or features can be used for scan registration, object tracking and robot navigation.

The method may find use in autonomous robots and driverless cars.

Differences between consecutive range data are used imaginatively to derive a novel measure of terrain unevenness, which in turn, is used for efficient segmentation of objects and features.

This paper aims to provide a solution to the first phase of a force-controlled circular Peg-In-Hole assembly using an industrial robot. The paper suggests motion planning of the robot’s end-effector so as to perform Peg-In-Hole search with minimum a priori information of the working environment.

The paper models Peg-In-Hole search problem as a problem of finding the minima in depth profile for a particular assembly. Thereafter, various optimization techniques are used to guide the robot to locate minima and complete the hole search. This approach is inspired by a human’s approach of searching a hole by moving peg in various directions so as to search a point of maximum insertion which is same as the minima in depth profile.

The usage of optimization techniques for hole search allows the robot to work with minimum a priori information of the working environment. Also, the iterative nature of the techniques adapts to any disturbance during assembly.

The techniques discussed here are quite useful if a force-controlled assembly needs to be performed in a highly unknown environment and also when the assembly setup can get disturbed in between.

The concept is original and provides a non-conventional use of optimization techniques, not for optimization of some process directly but for an industrial robot’s motion planning.

The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan.

The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region.

This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll.

The method applies on single 3D scans and not on aggregated point cloud in general.

The method has been tested on a mobile robot in outdoor environment in our research centre.

This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles.

Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

The focus of the paper is only on the contributions toward the use of entropy generation of non-Newtonian Casson fluid over an exponential stretching sheet. The purpose of this paper is to investigate the entropy generation and homogeneous–heterogeneous reaction. Velocity and thermal slips are considered instead of no-slip conditions at the boundary.

Basic equations in form of partial differential equations are converted into a system of ordinary differential equations and then solved using the spectral quasi-linearization method (SQLM).

The validity of the model is established using error analysis. Variation of the velocity, temperature, concentration profiles and entropy generation against some of the governing parameters are presented graphically. It is to be noted that the increase in entropy generation due to increase in heterogeneous reaction parameter is due to the increase in heat transfer irreversibility. It is further noted that the Bejan number decreases with Brinkman number because increase in Brinkman number reduces the total entropy generation.

This paper acquires realistic numerical explanations for rapidly convergent temperature and concentration profiles using the SQLM. Convergence of the numerical solutions was monitored using the residual error of the PDEs. The resulting equations are then integrated using the SQLM. The influence of emergent flow, heat and mass transfer parameters effects are shown graphically.