In this article, we present a distributed algorithm to solve a class of multirobot task assignment problems. We formulate task assignment as a mathematical optimization and solve for optimal solutions with a variant of the consensus alternating direction method of multipliers (C-ADMM). We provide C-ADMM-based algorithms for both the primal and dual problem formulations and show the advantages of each form depending on the problem specifics. In our algorithm, each robot solves a series of local optimization problems and communicates the results to its local neighbors, ultimately converging to an optimal task assignment in problems with linear objective functions and an optimal solution of the relaxed problem in convex problems. While many other distributed algorithms require a central station for their implementation, in our algorithm, each robot only communicates with its one-hop neighbors. In linear task assignment problems, our algorithm converges to the optimal task assignment, unlike many other distributed algorithms for this problem, which yield suboptimal solutions. We demonstrate our algorithms in task assignment problems over a variety of communication network topologies, where we show that our inexact dual algorithm is at least 60% faster than other distributed algorithms, which produce an optimal task assignment. In addition, our dual algorithm attains a 69% speedup in computation time compared to a notable distributed variant of the Hungarian method (Chopra et al., 2017). We also apply our algorithm to a multi-unmanned-aerial-vehicle persistent surveillance problem, showing its suitability for problems involving periodic task assignments.

This paper proposes a distributed deep reinforcement learning (RL) based strategy for a team of Unmanned Aerial Vehicles (UAVs) to autonomously fight forest fires. We first model the forest fire as a Markov decision process (MDP) with a factored structure. We consider optimally controlling the forest fire without agents using dynamic programming, and show any exact solution and many approximate solutions are computationally intractable. Given the problem complexity, we consider a deep RL approach in which each agent learns a policy requiring only local information. We show with Monte Carlo simulations that the deep RL policy outperforms a hand-tuned heuristic, and scales well for various forest sizes and different numbers of UAVs as well as variations in model parameters. Experimental demonstrations with mobile robots fighting a simulated forest fire in the Robotarium at the Georgia Institute of Technology are also presented.

With no initial angular momentum an ordinary house cat is capable of flipping over onto its feet in mid-air and landing safely after a fall. As the field of robotics advances and robots become more dynamic, control algorithms for landing safely from a long, intended fall will become more necessary. Here we present an algorithm that leverages nonholonomic trajectory planning inspired by the falling cat to orient an articulated robot through configuration changes to achieve a pose that reduces the impact at landing. The calculated impact pose results in minimal loss of energy through rolling, while maximizing the rolling time. In addition to orienting and rolling, our controller guides the system to behave like a damped spring-mass system to reduce the magnitude of contact forces. Our framework is general and is applicable to systems that can be modeled as a connected tree of rigid bodies. We illustrate the feasibility of the algorithm through simulation and physical experiments with a planar three-link robot.