This paper address the challenges encountered by developers when deploying a distributed decision-making behavior on heterogeneous robotic systems. Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited if relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded intelligence, the sensitivity to the network topology, and render the deployment on physical robots tedious and error-prone. By integrating the swarm-oriented programming language Buzz with the standard environment of ROS, this work demonstrates that behaviors requiring distributed consensus can be successfully deployed in practice. From simulation to the field, the behavioral script stays untouched and applicable to heterogeneous robot teams. We present the software structure of our solution as well as the swarm-oriented paradigms required from Buzz to implement a robust generic consensus strategy. We show the applicability of our solution with simulations and experiments with heterogeneous ground-and-air robotic teams.
Swarms of UAVs are challenging to implement, but their high potential to be robust, resilient and flexible @cite_20 motivates a number of robotics laboratories. For instance, the Ecole Polytechnique Federale de Lausanne Laboratory of Intelligent Systems @cite_21 introduced fixed-wing UAVs to demonstrate flocking @cite_22 with platform specific programming. Flocking is part of basic swarm behaviors that do not require formal consensus over the group @cite_15 .
@cite_15 @cite_21 @cite_22 @cite_20
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In this paper, we study the distributed optimal flocking control problem of multiple Unmanned Aircraft Systems (Multi-UAS). Using the emerging Neuro Dynamic Programming (NDP) technique, a novel distributed near optimal flocking design is proposed for ensuring the multi-UAS to follow the three heuristic flocking rules (i.e. cohesion, separation and alignment) in an optimal manner. First, an innovative cost function is developed by incorporating system cohesion, separation and alignment performance together. Subsequently, a novel neural network (NN) is proposed to approximate the minimized cost function value by using Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Moreover, the near optimal flocking can be attained by minimizing estimated cost function. Using standard Lyapunov stability analysis, the uniformly ultimately boundedness (UUB) of the closed-loop multi-UAS is verified. Simulation results demonstrate the effectiveness of proposed scheme. Control and coordination of a set of autonomous vehicles that can freely move on a plane is a widely studied topic in robotics. The focus on this kind of problem has grown in recent years because of the increased interest in studying systems populated by many, simple units, instead of few, powerful ones. In particular, these units simply observe the environment by using their sensors, and react following simple rules: the reaction to the environmental stimuli is called the behavior of the unit. In this paper we study the flocking problem : a set of mobile units are required to follow a leader unit while keeping a predetermined formation (i.e., they are required to move in flock, like a group of soldiers). Moreover, the units in the flock do not know beforehand the path the leader will take: their task is just to follow him wherever he goes, and to keep the formation while moving. Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints. An alternative to deploying a single robot of high complexity can be to utilise robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.