Motion Planning for Cooperative Multi Robot Target Following Systems

Abstract

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. For such tasks, 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. Each of the above mentioned contributions are validated extensively in synthetic environments. We showcase real-world experimental results for the proposed perception-driven model-based and model-free optimal control algorithms for target following newline