Kaushik Das

Utsav Rai, Hardik Mehta, Vismay Vakharia et al.

The proposed system outlined in this paper is a solution to a use case that requires the autonomous picking of cuboidal objects from an organized or unorganized pile with high precision. This paper presents an efficient method for precise pose estimation of cuboid-shaped objects, which aims to reduce errors in target pose in a time-efficient manner. Typical pose estimation methods like global point cloud registrations are prone to minor pose errors for which local registration algorithms are generally used to improve pose accuracy. However, due to the execution time overhead and uncertainty in the error of the final achieved pose, an alternate, linear time approach is proposed for pose error estimation and correction. This paper presents an overview of the solution followed by a detailed description of individual modules of the proposed algorithm.

Raunak Srivastava, Roshan Sah, Kaushik Das

Attitude estimation or determination is a fundamental task for satellites to remain effectively operational. This task is furthermore complicated on small satellites by the limited space and computational power available on-board. This, coupled with a usually low budget, restricts small satellites from using high precision sensors for its especially important task of attitude estimation. On top of this, small satellites, on account of their size and weight, are comparatively more sensitive to environmental or orbital disturbances as compared to their larger counterparts. Magnetic disturbance forms the major contributor to orbital disturbances on small satellites in Lower Earth Orbits (LEO). This magnetic disturbance depends on the Residual Magnetic Moment (RMM) of the satellite itself, which for higher accuracy should be determined in real-time. This paper presents a method for in-orbit estimation of the satellite magnetic dipole using a Random Walk Model in order to circumnavigate the inaccuracy arising due to unknown orbital magnetic disturbances. It is also ensured that the dipole as well as attitude estimation of the satellite is done using only a magnetometer as the sensor.

Roshan Sah, Raunak Srivastava, Kaushik Das

A constellation of remote sensing small satellite system has been developed for infrastructure monitoring in India by using SAR Payload. The LEO constellation of the small satellites is designed in a way, which can cover the entire footprint of India. Since India lies a little above the equatorial region, the orbital parameters are adjusted in a way that inclination of 36 degrees and RAAN varies from 70-130 degrees at a height of 600 km has been considered. A total number of 4 orbital planes are designed in which each orbital plane consisting 3 small satellites with 120-degrees true anomaly separation. Each satellite is capable of taking multiple look images with the minimum resolution of 1 meter per pixel and swath width of 10 km approx. The multiple look images captured by the SAR payload help in continuous infrastructure monitoring of our interested footprint area in India. Each small satellite is equipped with a communication payload that uses X-band and VHF antenna, whereas the TT&C will use a high data-rate S-band transmitter. The paper presents only a coverage metrics analysis method of our designed constellation for our India footprint by considering the important metrics like revisit time, response time, and coverage efficiency. The result shows that the average revisits time for our constellation ranges from about 15- 35 min which is less than an hour and the average response time for this iteratively designed constellation ranges from about 25-120 min along with hundred percent coverage efficiency most of the time. Finally, it was concluded that each satellite has 70kg of total mass and costs around $ 0.75M to develop.

Nithya Sridhar, Sai Abhinay. N, Chaithanya Krishna. B et al.

This work proposes a systematic framework for modelling and controller design of a Commercial-Off-The Shelf (COTS) unmanned rotorcraft using control theory and principles, for brick wall construction. With point to point navigation as the primary application, command velocities in the three axes of the Unmanned Aerial Vehicle (UAV) are considered as inputs of the system while its actual velocities are system outputs. Using the sine and step response data acquired from a Hardware-in-Loop (HiL) test simulator, the considered system was modelled in individual axes with the help of the proposed framework. This model was employed for controller design where a sliding mode controller was chosen to satisfy certain requirements of the application like robustness, flexibility and accuracy. The model was validated using step response data and produced a deviation of only 9%. Finally, the controller results from field test showed fine control up to 8 cms accuracy. Sliding Mode Control (SMC) was also compared with a linear controller derived from iterative experimentations and seen to perform better than the latter in terms of accuracy, and robustness to parametric variations and wind disturbances.

Rajashekhar V S, Rokesh Laishram, Kaushik Das et al.

In this paper, a 1-DoF gripper mechanism has been synthesized for the type of mechanism, number of links and joints, and the dimensions of length, width and thickness of links. The type synthesis is done by selecting the proper class of mechanism from Reuleaux's six classes of mechanisms. The number synthesis is done by using an algebraic method. The dimensions of the linkages are found using the geometric programming method. The gripper is then modeled in a computer aided design software and then fabricated using an additive manufacturing technique. Finally the gripper mechanism with DC motor as an actuator is mounted on an Unmanned Aerial Vehicle (UAV) to grip a spherical object moving in space. This work is related to a task in challenge 1 of Mohamed Bin Zayed International Robotics Challenge (MBZIRC)-2020.

V. SRajashekhar, M. R. Vibha, Kaushik Das et al.

Aerial transportation requires a simple yet reliable gripper for picking and placing objects of interest. In this work, we design an aerial gripper for passive grasping and impulsive release of ferrous coated objects. Permanent magnets are used for passive grasping and the Scotch Yoke mechanism is used for providing impulsive force to drop the object. The load carrying capacity of the gripper is calculated theoretically and experimentally. The parameters such as the radius of the rotating disk and length of the slider in the Scotch Yoke mechanism were optimized using weighted geometric programming. The dimensions of the gripper mount were derived considering the various components of the gripper. The gripper was mounted on an Unmanned Aerial Vehicle (UAV) and the tests were done by carrying ferrous coated cuboid shaped objects of different sizes and masses. These tests were done in manual and autonomous mode in the outdoor environment.

Sarthak J. Shetty, Rahul Ravichandran, Lima Agnel Tony et al.

Survivors stranded during floods tend to seek refuge on dry land. It is important to search for these survivors and help them reach safety as quickly as possible. The terrain in such situations however, is heavily damaged and restricts the movement of emergency personnel towards these survivors. Therefore, it is advantageous to utilize Unmanned Aerial Vehicles (UAVs) in cooperation with on-ground first responders to aid search and rescue efforts. In this article we demonstrate an implementation and improvement of the weight-based path planning algorithm using an off-the-shelf UAV. The coordinates of the survivor and their heading is reported by an on-ground observer to the UAV to generate a weighted map of the surroundings for exploration. Each coordinate in the map is assigned a weight which dictates the priority of exploration. These waypoints are then sorted on the basis of their weights to arrive at an ordered list for exploration by the UAV. We developed the model in MATLAB, followed by prototyping on Robot Operating System (ROS) using a 3DR Iris quadcopter. We tested the model on an off-the-shelf UAV by utilizing the MAVROS and MAVLINK capabilities of ROS. During the implementation of the algorithm on the UAV, several additional factors such as unreliable GPS signals and limited field of view which could effect the performance of the model were in effect, despite which the algorithm performed fairly well. We compared our model with conventional algorithms described in the literature, and showed that our implementation outperforms them.

Madhu Babu, Swagat Kumar, Anima Majumder et al.

In this paper, we provide an improved version of UnDEMoN model for depth and ego motion estimation from monocular images. The improvement is achieved by combining the standard bi-linear sampler with a deep network based image sampling model (DIS-NET) to provide better image reconstruction capabilities on which the depth estimation accuracy depends in un-supervised learning models. While DIS-NET provides higher order regression and larger input search space, the bi-linear sampler provides geometric constraints necessary for reducing the size of the solution space for an ill-posed problem of this kind. This combination is shown to provide significant improvement in depth and pose estimation accuracy outperforming all existing state-of-the-art methods in this category. In addition, the modified network uses far less number of tunable parameters making it one of the lightest deep network model for depth estimation. The proposed model is labeled as "UnDEMoN 2.0" indicating an improvement over the existing UnDEMoN model. The efficacy of the proposed model is demonstrated through rigorous experimental analysis on the standard KITTI dataset.

Madhu Babu, Anima Majumder, Kaushik Das et al.

This paper presents an unsupervised deep learning framework called UnDEMoN for estimating dense depth map and 6-DoF camera pose information directly from monocular images. The proposed network is trained using unlabeled monocular stereo image pairs and is shown to provide superior performance in depth and ego-motion estimation compared to the existing state-of-the-art. These improvements are achieved by introducing a new objective function that aims to minimize spatial as well as temporal reconstruction losses simultaneously. These losses are defined using bi-linear sampling kernel and penalized using the Charbonnier penalty function. The objective function, thus created, provides robustness to image gradient noises thereby improving the overall estimation accuracy without resorting to any coarse to fine strategies which are currently prevalent in the literature. Another novelty lies in the fact that we combine a disparity-based depth estimation network with a pose estimation network to obtain absolute scale-aware 6 DOF Camera pose and superior depth map. The effectiveness of the proposed approach is demonstrated through performance comparison with the existing supervised and unsupervised methods on the KITTI driving dataset.