Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.
Distributed or decentralized approaches to the problem are seen less frequently. Distributed collaborative transport was achieved for wheeled robots, pushing a rigid geometric object @cite_1 with a combination of potential field forces. More recently, two UAVs were designed to cooperate in a fully decentralized configuration for the transport of a rigid body using only inertial measurement and vision @cite_0 . Both are inspiring works, but are still far from the target of this work.
@cite_0 @cite_1
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Micro aerial vehicles have the potential to assist humans in tasks such as manipulation and transportation for construction and humanitarian missions, beyond simply acquiring data and building maps. In this letter, we address the state estimation, control, and trajectory planning in cooperative transportation of structures, which are either too heavy or too big to be carried by small microvehicles. Specifically, we consider small quadrotors, each equipped only with a single camera and inertial measurement unit as a sensor. The key contributions are 1) a new approach to coordinated control, which allows independent control of each vehicle while guaranteeing the system's stability and 2) a new cooperative localization scheme that allows each vehicle to benefit from measurements acquired by other vehicles. The latter relies on the vehicles exploiting the inherent rigid structure information to infer additional constraints between the vehicles' poses allowing us to formulate the pose estimation problem as an optimization problem on the Lie group SE(3). The proposed approach is validated through experimental results with multiple quadrotors grasping and transporting a rigid structure. This paper addresses the deployment problem for a swarm of autonomous mobile robots initially randomly distributed in 3 dimensional space. A fully decentralized geometric self-configuration approach is proposed to deploy individual robots at a given spatial density. Specifically, each robot interacts with three neighboring robots in a selective and dynamic fashion without using any explicit communication so that four robots eventually form a regular tetrahedron. Using such local interactions, the proposed algorithms enable a swarm of robots to span a network of regular tetrahedrons in a designated space. The convergence of the algorithms is theoretically proved using Lyapunov theory. Through extensive simulations, we validate the effectiveness and scalability of the proposed algorithms. We propose a distributed algorithm for collaborative localization of multiple autonomous robots that fuses inter-robot relative measurements with odometry measurements to improve upon dead reckoning estimates. It is an extension of our previous work [6], in which a method for fusing inter-robot pose measurements was presented. In this paper we extend the method to fuse any type of inter-robot measurements (distance, bearing, relative position, relative orientation, and any combination thereof), thus increasing the applicability of the method. The proposed method is posed as an optimization problem in a product Riemannian manifold; and is solved by gradient descent without performing a parameterization of the orientations. The proposed distributed algorithm allows each robot to compute its own pose estimate based on local measurements and communication with its neighbors. Simulations show that the proposed distributed algorithm significantly improves localization accuracy over the case of no-collaboration. Simulations show that, in some situations, the proposed distributed algorithm outperforms two competing methods - an Extended Kalman Filter-based algorithm as well as a distributed pose graph optimization method that relies on an Euclidean parameterization of orientations. Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-robot partially observable Markov decision processes (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. In addition, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by exploiting a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper presents two different applications: environmental monitoring with unmanned aerial vehicles (UAVs); and cooperative tracking, in which several robots have to jointly track a moving target of interest. The first one is used as a proof of concept and illustrates the proposed ideas through different simulations. The second one adds real multi-robot experiments, showcasing the flexibility and robust coordination that our techniques can provide.