Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Aerial Human-Comfortable Collision-free Navigation in Dense Environments

With current overuse of the road transportation system and planned increase in traffic, inno- vative solutions that overcome environmental and financial cost of the current system should be assessed. A promising idea is the use of the third dimension for personal transportation. Therefore, the European project myCopter, funded under the 7th framework, aimed at en- abling the technologies for Personal Aerial Transportation Systems as breakthrough in 21st century transportation systems. This project was the starting point of this thesis. When multiple vehicles share a common part of the sky, the biggest challenge is the man- agement of the risk of collision. While optimal collision-free navigation strategies have been proposed for autonomous robots, trajectories and accelerations for Personal Aerial Vehicles (PAVs) should also take into account human comfort for their passengers, which has rarely been the focus of these studies. Comfort of the trajectories is a key factor in order for this new transportation mean to be accepted and adopted by everyday users. Existing strategies used to maximize human-comfort of trajectories are based on path planning strategies, which compute beforehand the whole trajectory, implementing comfort as an optimization criteria. Personal Aerial Transportation Systems will have a high density of vehicles, where the time to react to potential threats might decrease to a few seconds only. This might be insufficient to compute a new trajectory each time using these path planning strategies. Therefore, in this thesis, a reactive decentralized strategy is proposed, maximizing the comfort of the trajectories for humans traveling in a Personal Aerial Vehicle. To prove the feasibility of collision avoidance strategies, it is not sufficient anymore to validate them only in simulation, but, in addition, real-time tests in a realistic outdoor environment should be performed. Nowadays, single drones can be effectively controlled by a single operator on the ground. The challenge relies instead on an efficient management of a whole swarm of drone. In this thesis, a framework to perform outdoor drone experiment was developed in order to validate the proposed collision avoidance strategy. On the one hand, an autopilot framework was developed, tailored for multi-drone experiments, allowing fast and easy deployment and maintenance of a swarm of drones. On the other hand, a ground control interface is proposed in order to monitor, control and maintain safety in a flight with a swarm of drones. Using the autopilot framework together with the ground control interface, the proposed collision avoidance strategy was validated using 10 quadrotors flying autonomously outdoor in a challenging scenario