We address the problem of progressively deploying a set of robots to a formation defined as a point cloud, in a decentralized manner. To achieve this, we present an algorithm that transforms a given point cloud into an acyclic directed graph. This graph is used by the control law to allow a swarm of robots to progressively form the target shape based only on local decisions. This means that free robots (i.e., not yet part of the formation) find their location based on the perceived location of the robots already in the formation. We prove that for a 2D shape it is sufficient for a free robot to compute its distance from two robots in the formation to achieve this objective. We validate our method using physics-based simulations and robotic experiments, showing consistent convergence and minimal formation placement error.

The stable and repeatable grasping of objects lying on a flat hard surface is addressed in this paper. A physical model of an object lying on a flat surface and its interaction with the environment and with a gripper is proposed. The important parameters governing the interaction are obtained. From this model, a grasping procedure is established and a robotic gripper is modified in order to grant the ability to pick up large thin objects lying on smooth hard surfaces. The procedure is implemented to demonstrate its repeatability on a chosen set of objects. It is shown that by sensing the force applied on the object and by taking advantage of the nature of the contact provided by the passive joint of the modified finger, a wide range of previously not directly graspable objects are made graspable via the application of a general approach. The experimental results reported clearly show the benefits of the simple force sensing implemented in the gripper as well as of the use of passive joints when interacting with very stiff environments. The proposed approach, while simple, yields a repeatable solution to a complex manipulation problem.

The coordination of robot swarms -large decentralized teams of robots -generally relies on robust and efficient inter-robot communication. Maintaining communication between robots is particularly challenging in field deployments where robot motion, unstructured environments, limited computational resources, low bandwidth, and robot failures add to the complexity of the problem. In this paper we propose a novel lightweight algorithm that lets a heterogeneous group of robots navigate to a target in complex 3D environments while maintaining connectivity with a ground station by building a chain of robots. The fully decentralized algorithm is robust to robot failures, can heal broken communication links, and exploits heterogeneous swarms: when a target is unreachable by ground robots, the chain is extended with flying robots. We test the performance of our algorithm using up to 100 robots in a physics-based simulator with three mazes and several robot failure scenarios. We then validate the algorithm with physical platforms: 7 wheeled robots and 6 flying ones, in homogeneous and heterogeneous scenarios in the lab and on the field.

Since the beginning of space exploration, Mars and the Moon have been explored with orbiters, landers, and rovers. Over forty missions have targeted Mars, and more than a hundred, the Moon. Developing novel strategies and technologies for exploring celestial bodies continues to be a focus of space agencies. Multi-robot systems are particularly promising for planetary exploration, as they are more robust to individual failure and have the potential to explore larger areas; however, there are limits to how many robots an operator can individually control. We recently took part in the European Space Agency's interdisciplinary equipment test campaign (PANGAEA-X) at a Lunar/Mars analogue site in Lanzarote, Spain. We used a heterogeneous fleet of Unmanned Aerial Vehicles (UAVs)-a swarm-to study the interplay of systems operations and human factors. Human operators directed the swarm via ad-hoc networks and data sharing protocols to explore unknown areas under two control modes: one in which the operator instructed each robot separately; and the other in which the operator provided general guidance to the swarm, which self-organized via a combination of distributed decision-making, and consensus building. We assessed cognitive load via pupillometry for each condition, and perceived task demand and intuitiveness via self-report. Our results show that implementing higher autonomy with swarm intelligence can reduce workload, freeing the operator for other tasks such as overseeing strategy, and communication. Future work will further leverage advances in swarm intelligence for exploration missions.

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