Crazyswarm: A large nano-quadcopter swarm
01 May 2017-pp 3299-3304
TL;DR: This work defines a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors and develops a method to reliably track many small rigid bodies with identical motion-capture marker arrangements for state estimation.
Abstract: We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed one-way data flow and supports a large number of vehicles per radio. We achieve reliable flight with accurate tracking (< 2 cm mean position error) by implementing the majority of computation onboard, including sensor fusion, control, and some trajectory planning. We provide various examples and empirically determine latency and tracking performance for swarms with up to 49 vehicles.
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TL;DR: The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping, and dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing.
Abstract: The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.
333 citations
18 Jul 2018
TL;DR: This paper numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities, and showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles.
Abstract: We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
285 citations
Cites background from "Crazyswarm: A large nano-quadcopter..."
...Autonomous drone swarms also appear in the scientific literature, using indoor motion capture–based (29, 30), outdoor Global Positioning System (GPS)–based (24, 31–33), or even vision-assisted (34, 35) navigation....
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TL;DR: The proposed method can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes, and is demonstrated on a quadrotor swarm navigating in a warehouse setting.
Abstract: We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.
228 citations
02 Apr 2020
TL;DR: This paper collects and categorizes swarm behaviors into spatial organization, navigation, decision making, and miscellaneous and gives a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market.
Abstract: In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.
209 citations
Cites methods from "Crazyswarm: A large nano-quadcopter..."
..., accelerometer, gyroscope, magnetometer, and a high precision pressure sensor (Preiss et al., 2017)....
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...1 49 (Preiss et al., 2017) Aggregation, collective exploration, coordinated motion,...
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...They use multiple sensors, e.g., accelerometer, gyroscope, magnetometer, and a high precision pressure sensor (Preiss et al., 2017)....
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TL;DR: Without any external infrastructures prepositioned, each agent cooperatively performs a consensus-based fusion, which fuses the obtained direct and indirect RL estimates, to generate the relative positions to its neighbors in real time despite the fact that some UAVs may not have direct range measurements to their neighbors.
Abstract: This puts forth an infrastructure-free cooperative relative localization (RL) for unmanned aerial vehicles (UAVs) in global positioning system (GPS)-denied environments. Instead of estimating relative coordinates with vision-based methods, an onboard ultra-wideband (UWB) ranging and communication (RCM) network is adopted to both sense the inter-UAV distance and exchange information for RL estimation in 2-D spaces. Without any external infrastructures prepositioned, each agent cooperatively performs a consensus-based fusion, which fuses the obtained direct and indirect RL estimates, to generate the relative positions to its neighbors in real time despite the fact that some UAVs may not have direct range measurements to their neighbors. The proposed RL estimation is then applied to formation control. Extensive simulations and real-world flight tests corroborate the merits of the developed RL algorithm.
166 citations
Cites background from "Crazyswarm: A large nano-quadcopter..."
...The UAVs can infer a relative estimate based on the shared data [3], [8]–[11]....
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...Examples include global positioning system (GPS) [8], [9]; motion tracking systems [3], [10]; and radio-based positioning with anchors such as ultra-wideband (UWB) networks [11]....
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References
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TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Abstract: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >
17,598 citations
09 May 2011
TL;DR: An algorithm is developed that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs.
Abstract: We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs. A nonlinear controller ensures the faithful tracking of these trajectories. Experimental results illustrate the application of the method to fast motion (5–10 body lengths/second) in three-dimensional slalom courses.
1,875 citations
"Crazyswarm: A large nano-quadcopter..." refers background or methods in this paper
...A simple linear transformation can compute the desired squared rotor speeds from Mdes and the projection of Fdes to the body z axis, as shown in [16]....
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...Quadcopter dynamics are differentially flat in the outputs y = (p, ψ), where ψ is the yaw angle in world coordinates [16]....
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...This controller is identical to the one presented in [16] except for the added integral terms....
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...Our controller is based on the nonlinear position controller of [16], augmented with integral terms for position and yaw error....
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TL;DR: In the last five years, advances in materials, electronics, sensors, and batteries havefueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and0.1-0.5 kg in mass.
Abstract: In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].
806 citations
"Crazyswarm: A large nano-quadcopter..." refers background in this paper
...Compared to architectures that evaluate trajectories on a PC and transmit attitude and thrust controls at a high rate [2], [3], we shift some planning effort onboard to reduce the needed radio bandwidth....
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...The group’s infrastructure is described in [2]....
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...In contrast to related systems [2], [3], we implement the majority of in-flight computation onboard....
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TL;DR: It is argued that the reduction in size leads to agility and the ability to operate in tight formations and experimental arguments in support of this claim are provided.
Abstract: We describe a prototype 75 g micro quadrotor with onboard attitude estimation and control that operates autonomously with an external localization system The motivation for designing quadrotors at this scale comes from two observations First, the agility of the robot increases with a reduction in size, a fact that is supported by experimental results in this paper Second, smaller robots are able to operate in tight formations in constrained, indoor environments We describe the hardware and software used to operate the vehicle as well our dynamic model We also discuss the aerodynamics of vertical flight and the contribution of ground effect to the vehicle performance Finally, we discuss architecture and algorithms to coordinate a team of these quadrotors, and provide experimental results for a team of 20 micro quadrotors
429 citations
"Crazyswarm: A large nano-quadcopter..." refers methods in this paper
...describe the design, planning, and control of a custom micro quadcopter with experiments involving up to 20 vehicles [1]....
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TL;DR: A method is presented for the rapid generation and feasibility verification of motion primitives for quadrocopters and similar multirotor vehicles, and it is shown that a millionmotion primitives may be evaluated and compared per second on a standard laptop computer.
Abstract: A method is presented for the rapid generation and feasibility verification of motion primitives for quadrocopters and similar multirotor vehicles. The motion primitives are defined by the quadrocopter's initial state, the desired motion duration, and any combination of components of the quadrocopter's position, velocity, and acceleration at the motion's end. Closed-form solutions for the primitives are given, which minimize a cost function related to input aggressiveness. Computationally efficient tests are presented to allow for rapid feasibility verification. Conditions are given under which the existence of feasible primitives can be guaranteed a priori . The algorithm may be incorporated in a high-level trajectory generator, which can then rapidly search over a large number of motion primitives which would achieve some given high-level goal. It is shown that a million motion primitives may be evaluated and compared per second on a standard laptop computer. The motion primitive generation algorithm is experimentally demonstrated by tasking a quadrocopter with an attached net to catch a thrown ball, evaluating thousands of different possible motions to catch the ball.
300 citations
"Crazyswarm: A large nano-quadcopter..." refers methods in this paper
...Our solution is similar to the motion primitive described in [19], but we constrain ....
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