456 research outputs found
tinySLAM-based exploration with a swarm of nano-UAVs
This paper concerns SLAM and exploration for a swarm of nano-UAVs. The laser
range finder-based tinySLAM algorithm is used to build maps of the environment.
The maps are synchronized using an iterative closest point algorithm. The UAVs
then explore the map by steering to points selected by a modified dynamic
coverage algorithm, for which we prove a stability result. Both algorithms
inform each other, allowing the UAVs to map out new areas of the environment
and move into them for exploration. Experimental findings using the nano-UAV
Crazyflie 2.1 platform are presented. A key challenge is to implement all
algorithms on the hardware limited experimental platform.Comment: Published at the Sixth International Symposium on Swarm Behavior and
Bio-Inspired Robotics 2023 (SWARM 6th 2023). Pages 899-90
SwarmLab: a Matlab Drone Swarm Simulator
Among the available solutions for drone swarm simulations, we identified a
gap in simulation frameworks that allow easy algorithms prototyping, tuning,
debugging and performance analysis, and do not require the user to interface
with multiple programming languages. We present SwarmLab, a software entirely
written in Matlab, that aims at the creation of standardized processes and
metrics to quantify the performance and robustness of swarm algorithms, and in
particular, it focuses on drones. We showcase the functionalities of SwarmLab
by comparing two state-of-the-art algorithms for the navigation of aerial
swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze
the variability of the inter-agent distances and agents' speeds during flight.
We also study some of the performance metrics presented, i.e. order, inter and
extra-agent safety, union, and connectivity. While Olfati-Saber's approach
results in a faster crossing of the obstacle field, Vasarhelyi's approach
allows the agents to fly smoother trajectories, without oscillations. We
believe that SwarmLab is relevant for both the biological and robotics research
communities, and for education, since it allows fast algorithm development, the
automatic collection of simulated data, the systematic analysis of swarming
behaviors with performance metrics inherited from the state of the art.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
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
RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System
Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great
potential for fast autonomous exploration, it has received far too little
attention. In this paper, we present RACER, a RApid Collaborative ExploRation
approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs,
a pairwise interaction based on an online hgrid space decomposition is used. It
ensures that all UAVs simultaneously explore distinct regions, using only
asynchronous and limited communication. Further, we optimize the coverage paths
of unknown space and balance the workloads partitioned to each UAV with a
Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task
allocation, each UAV constantly updates the coverage path and incrementally
extracts crucial information to support the exploration planning. A
hierarchical planner finds exploration paths, refines local viewpoints and
generates minimum-time trajectories in sequence to explore the unknown space
agilely and safely. The proposed approach is evaluated extensively, showing
high exploration efficiency, scalability and robustness to limited
communication. Furthermore, for the first time, we achieve fully decentralized
collaborative exploration with multiple UAVs in real world. We will release our
implementation as an open-source package.Comment: Conditionally accpeted by TR
Signal-based self-organization of a chain of UAVs for subterranean exploration
Miniature multi-rotors are promising robots for navigating subterranean
networks, but maintaining a radio connection underground is challenging. In
this paper, we introduce a distributed algorithm, called U-Chain (for
Underground-chain), that coordinates a chain of flying robots between an
exploration drone and an operator. Our algorithm only uses the measurement of
the signal quality between two successive robots as well as an estimate of the
ground speed based on an optic flow sensor. We evaluate our approach formally
and in simulation, and we describe experimental results with a chain of 3 real
miniature quadrotors (12 by 12 cm) and a base station
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
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