Publications

Irrigation canal networks serve as the bedrock of agriculture sectors across the globe as they are the primary channel through which water runs from major sources to agricultural lands. However, the water-carrying capacity of these water channels significantly reduces over time because of erosion, structural deterioration, and silt accumulation. As a result, routine inspections are required to analyze and repair these water channels which necessitates automation because of the vast length of these channels. We present a framework that enables Micro-Aerial Vehicles(MAVs) not only to navigate in an unknown cluttered canal environment but also to provide a complete 3-Dimensional map for the inspection. The framework consists of three main components (mapping, path planning, and mission planner) that gradually explore the environment while solving for start to local goal queries. We use Octomap; an octree-based representation of the environment for mapping, and we extended the Informed Rapidly-exploring Random Tree (Informed-RRT*) for optimal path planning and replan paths with respect to the static nearby and dynamic obstacles perceived during the execution of the mission. A simulated 2,378 meters length of canal environment is implemented and demonstrated by using the Airsim simulation in the Unreal engine, running on Robot Operation System (ROS) and Linux OS. Results obtained show that the framework enables the MAV to navigate over a simulated canal environment and allows the MAV to map the 3D structure of the canal.

Motion planning for a snake robot in an unknown complex environment is a long-standing research problem because of the complex control of the modular mechanism. We propose deep reinforcement learning-based novel framework for motion planning. In this model-free framework, we propose a double deep Q-leaming-based technique to learn the optimal policy for reaching the goal point from a random start point; in a minimum number of steps in various unknown environments. In this approach, the agent learns to minimize the distance between the current and goal positions by aligning its yaw angle to the goal points through controlling multiple locomotive gaits. For experimental evaluation, we trained and tested the model in obstacle-free terrains. For training, we selected the model on the mud-terrain and tested for 50 episodes on five different terrains concrete, default, metallic, mud, and wooden. From simulation results, we observe the learned-optimal policy shows promising results for all unknown environments with a performance efficiency of 100% for all terrains except the wooden-terrain where it fails for only one episode and achieves 98% efficiency.

For optimal agriculture yields, the land designated for agriculture requires proper irrigation. The major part of Pakistan’s irrigation system is served through canals that are outflows of dams, barrages, and rivers. The total length of the irrigating canal network in the Indus Basin is about 57,000 kilometers[47]. The manual labor for inspecting and analyzing the condition of water canals and then repairing during the closure period each year cannot be possible by laborious manual inspection. Hence, there is a need for automation in inspection tasks related to the water channels. The capability of moving robots in accomplishing desired tasks with the help of a couple of sensors mounted on them, not only decreases computation time but guarantees task completion as well. To be able to use robots for canal-like environments, one requirement is obstacle avoidance. We have developed a near to real canal environment in the Unreal engine, with real textures of trees, brushes, and bridges. Various conceivable obstacles that an aerial vehicle can confront in a canal-like environment is added in the simulation. Robot Operating System (ROS)[10] plays an important role in the simulation as it offers methods and groups of useful libraries (libraries for frame transformations, point cloud processing, visualization, and data monitoring to name a few). The Airsim plugin[55] in the Unreal engine[11] is used to simulate the MAV in the canal. We used Stereo image P roc[9] for disparity and point cloud construction, Octomap server[61] for Octomap Mapping, Informed RRT *[31] for path planning, and Airsim Simple FlightController[55] for vehicle control. The vehicle is capable of profiling the canal channels quickly and effectively that will assist the human operator in surveying the canal during an annual canal closure. The developed system autonomously flies over the canal, not only build a threedimensional map of the canal environment but detects obstacles in the path of the vehicle and eventually avoid these obstacles.