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Sirineni Yahnit – Dual Degree CSE

Sirineni Yahnit  received his MS  Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Prof. Kamal Karlapalem. Here’s a summary of  Sirineni Yahnit‘s Decentralized Collision Avoidance and Motion Planning for Multi-RobotPayload Transport Systems:

In this work, we present a decentralized collision avoidance and motion planning algorithm for multi-robot payload transport systems (PTS) navigating through dynamic environments that has applications in various industries, assembly lines, and warehouses for transporting several payloads simultaneously by multiple PTS from one place to another. A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a rigid or deformable payload. Each PTS has one leader and multiple followers where the followers maintain a desired orientation with respect to the leader. Each PTS navigates through an environment having several other PTS and static obstacles. The objective of each PTS is to (a) Remain in formation all the time, (b) Reach the desired destination in minimum time and with minimum deviation from the desired path, (c) Avoid collisions with other PTS and static obstacles and, (d) Navigate through narrow spaces by respecting deformability constraints of the payload. The terms PTS and formation are used interchangeably in this thesis. Real-time collision avoidance for such systems is challenging due to the deformability of formations, high dimensional multi-robot non-convex workspaces, and heterogeneity in the type of PTS. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. We initially address the problem for rigid formations where we assume the payloads being carried to be rigid. We propose the Formation-ORCA (FORCA) algorithm for decentralized collision avoidance and motion planning of rigid payload transport systems. FORCA is a combination of optimal reciprocal collision avoidance (ORCA) and decentralized leader-follower formation control algorithms. We later address the problem for deformable formations where the formations can be deformed based on the deformability limits of the payload being carried. We present the PotentialField-Formation-ORCA-MPC (PF-ORCA-MPC) for decentralized collision avoidance and motion planning of deformable payload transport systems and has primarily two main steps to plan the motion of each formation. First, we compute collision-free multi-scale convex workspaces over a planning horizon using a combination of ORCA and repulsive potential fields. Subsequently, we compute the motion plans of formation over a horizon by proposing a novel formulation for collision avoidance, and we leverage a model predictive controller (MPC) to solve the problem. FORCA and PF-FORCA-MPC algorithms are validated through extensive simulations varying in i) number of formations, ii) configuration of formations, iii) number of static obstacles, iv) density and complexity of environments. Through a narrow corridor simulation, we show the ability of formations to deform themselves in order to squeeze in through tight spaces. We show that there is roughly 10\% decrease in time taken for the formations to reach their destinations when deformability could be exploited using PF-FORCA-MPC. Additionally, we show that we attain a low net average execution time of 0.025(s) per iteration, even in high-density cases with around 60 robots in the vicinity. The results validate that our solution facilitates real-time navigation of multiple formations allowing the formations to successfully reach their destinations while remaining in formation and also avoiding collisions. The proposed approach applies to holonomic robots as well, provided a holonomic version of formation controller is used. The algorithm does not make any assumptions regarding the size and number of robots in the formation. Hence, it can be incorporated in any general payload transport system of arbitrary size and configuration, as shown in the simulations. Being a decentralized method, our algorithms computationally scale well with an increase in the number of robots and formations used. We additionally conduct experiments in the gazebo simulator and present a proof of concept using real robots.