There is an acute shortage of human coconut tree climbers to harvest coconuts in India and other developing countries. The consequential diminished availability of skilled tree-climbers, to pass along the trade secrets to the succeeding generations, highlights the exigent need for an unmanned coconut-tree-climber as an alternative solution. This research presents Amaran, a novel robotic coconut tree climber and harvester. Amaran is comprised of a harvester with a distinctive robotic arm (RA) and a cutter, which can be attached to a ring-shaped frame that can be fastened to a coconut tree. Amaran can be controlled, through a wireless interface, either by an operator on the ground or via a smartphone-based app. Once dispositioned in the vicinity of the coconut bunch, the cutter is commanded to harvest the coconuts, by cutting the stem of the bunch attached to the tree. Verification of the Amaran robot's viability was confirmed by tests conducted on a coconut tree setup in the design lab. Subsequently, the design of the Amaran robot was validated by field tests in a coconut farm. In this paper, we discuss the design of the Amaran body and the harvester. Amaran's dexterity in climbing and harvesting coconuts can be challenged by height, circumference, and inclination of the tree. Experimental results show that Amaran can successfully climb trees up to 15.2 m tall, of circumferences ranging from 0.66 m to 0.92 m, and tree trunk inclinations of up to 30 degrees.

Many accidents happen while driving due to false estimation of the tailgating vehicle's distance and even while backing up or parking the vehicles in a parking lot. It is very important for the driver to know how close the tailgating vehicle. It is equally important to know the vicinity of the other vehicles and objects while parking or taking vehicle from the parking lots. The main objective of this paper is to accurately estimate the distance of a specific object by approximating the camera to be an ideal pinhole camera. In this method we use triangle similarity to estimate the distance after obtaining the focal length (in pixels) of the camera. Using image processing techniques we detect the object for which we want to estimate the distance. We input the known width of the object and using the calculated focal length, the algorithm calculates the distance after identifying the object. The experimental results are very promising that the estimation of the distance of an object using pin hole approximation would be extremely useful in vehicles to avoid accidents.

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

HHoneycomb, a self-reconfigurable cleaning robot, is designed based on tiling theory, to overcome the significant challenges experienced by the fixed morphology cleaning robot. It consists of four regular hexagonal units and the units are connected by a planar revolute joint which helps in reconfiguration. This platform attains six distinct configurations (bar, bee, arch, wave, worm, and pistol) and these configurations have circular arcs and irregular concave and convex boundary that would help in accessing various obstacles in the cleaning space. This work addresses the mechanical design, system-level modeling, reconfiguration of the platform via hinged joint mechanism, mobility of the platform, polyhex based tiling set, and power consumption during reconfiguration. The strength of the mechanical structure is studied based on the structural analysis of the system using finite element method. Based on the natural frequency and deformation pattern, the proposed design is validated and proven to overcome structural failure and system resonance. The kinematics formulation of the platform during locomotion and dynamics of each block during reconfiguration are derived. The robotic system is modeled in Simscape multibody toolbox of Matlab and the mobility of the platform is studied using the numerical simulation. Based on the real-time current consumption of each joint during reconfiguration, the energy efficient configuration and tiling set are addressed.

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.