Rahul Tallamraju received his doctorate in Computer Science and Engineering. His research work was supervised by Prof. Kamal Karlapalem. Here’s a summary of Rahul Tallamraju’s thesis, Motion Planning for Cooperative Multi-Robot Target Following Systems as explained by him:
Motion planning deals with the computation of a sequence of robot states and control actions to navigate between a start and a goal robot state. As part of this planning, a robot must detect environmental objects, monitor their changes, reason about them, and finally compute kinodynamically feasible real time trajectories for task-specific navigation. The work in this thesis is motivated by real-time cooperative motion planning applications. Such applications require multiple robots to work together and complete a common task through communication and negotiation. Furthermore, robots must leverage cooperation to improve sensing, thereby, allowing them to develop a shared understanding of the environment and plan their motion effectively. In our work, we primarily focus on the cooperative task of target following and its applications. The algorithms developed aim to be applicable towards aerial outdoor motion capture, search and rescue (SAR) applications for victim and rescuer tracking, and human target guided cooperative load transport applications. For the tasks mentioned above, computing real-time coordinated plans is challenging because of the associated high-dimensional multi-agent configuration space, non-linear inter-agent dependencies like collision-avoidance, limited communication bandwidth and range, non-convex and discontinuous perceptual constraints of common target perception and tracking, and, kinodynamically constrained common payload manipulation and transportation. The key contributions of this thesis that overcome the challenges mentioned above are as follows. We first develop a decentralized robot motion planning pipeline for target following, which mitigates the large planning configuration space problem. Additionally, we embed non-convex collision avoidance and inter-robot constraints into locally convex approximations, promoting the use of convex optimization methodologies for real-time planning. Model-predictive control facilitates high-frequency replanning, which handles imperfect system models, noisy observations, and communication limitations. Model-based optimal control algorithms and model-free sequential decision-making algorithms are leveraged to plan motion in perceptually driven tasks. Finally, a hierarchical decomposition of the complex kinodynamically constrained planning problem is proposed to compute feasible and safe trajectories in real-time. In the first part of the thesis, we introduce a novel decentralized multi-agent algorithm for real-time target following and collision avoidance using model-predictive control (MPC). The algorithm is the first of its kind convex-optimization-MPC approach and is a comprehensive solution to multi-agent collision avoidance for cooperative target following scenarios. The method is validated extensively in synthetic environments. In the second part, we empirically develop stochastic target perception models and embed it into the previously designed decentralized MPC to compute formation motion plans for cooperative target following. The controller actively computes robot control actions that minimize the fused uncertainty in target perception, thereby, binding perception and planning into a single MPC. Subsequently, we leverage model-free deep reinforcement learning algorithms to determine robot motion plans, which are optimized for target pose and shape estimation. Furthermore, we experimentally compare the performance of model-free and model-based approaches for the target following task. Both the approaches are evaluated and validated extensively in simulation and using real-robot experiments for the task of outdoor motion capture. In the third part, we analyze the challenges in kinematic planning for multirobot spatial payload manipulation and target following, and introduce a hierarchical motion planning solution that builds on the previously designed decentralized MPC. The performance is corroborated in challenging simulation environments. In the last part, we present a hierarchical energy-conscious control allocation algorithm which is constrained kinodynamically by multiple agents and electrically constrained by the actuators and battery limitations. The approach is the first of its kind control allocation algorithm, which models a multi-robot system as an over-actuated multi-agent system to plan its motion. The controller is verified in simulation, and a preliminary experimental testbed design is showcased for physical validation. We conclude this thesis by summarizing our novel model-predictive control formulation for the perceptually driven task of outdoor motion capture. Subsequently, we outline the key observations from the model-free approach for designing controllers for the same perceptually driven task. We then discuss the extension of the model-based target following controller towards planning motion of a high degree of freedom cooperative payload manipulation systems. Finally, we detail our conclusions from the reformulation of the mutli-robot payload transport system as an electromechanically constrained overactuated system. We detail all planned future research directions in each of the problems addressed in this thesis.