Collision-aware Task Assignment for Multi-Robot Systems

We propose a novel formulation of the collision-aware task assignment (CATA) problem and a decentralized auction-based algorithm to solve the problem with optimality bound. Using a collision cone, we predict potential collisions and introduce a binary decision variable into the local reward function for task bidding. We further improve CATA by implementing a receding collision horizon to address the stopping robot scenario, i.e. when robots are confined to their task location and become static obstacles to other moving robots. The auction-based algorithm encourages the robots to bid for tasks with collision mitigation considerations. We validate the improved task assignment solution with both simulation and experimental results, which show significant reduction of overlapping paths as well as deadlocks

Swarm Relays: Distributed Self-Healing Ground-and-Air Connectivity Chains

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. Unstructured environments, limited computational resources, low bandwidth, and robot failures all contribute to the complexity of connectivity maintenance. In this paper, we propose a novel lightweight algorithm to navigate a group of robots in complex environments while maintaining connectivity by building a chain of robots. The algorithm is robust to single robot failures and can heal broken communication links. The algorithm works in 3D environments: when a region is unreachable by wheeled robots, the chain is extended with flying robots. We test the performance of the algorithm using up to 100 robots in a physics-based simulator with three mazes and different robot failure scenarios. We then validate the algorithm with physical platforms: 7 wheeled robots and 6 flying ones, in homogeneous and heterogeneous scenarios.Comment: 9 pages, 8 figures, Accepted for publication in Robotics and Automation Letters (RAL

ACHORD: communication-aware multi-robot coordination with intermittent connectivity

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCommunication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves situational awareness. Exploring comms-restricted (e.g., subterranean) environments requires a multi-robot system to tolerate and anticipate intermittent connectivity, and to carefully consider comms requirements, otherwise mission-critical data may be lost. In this paper, we describe and analyze ACHORD (Autonomous & Collaborative High-Bandwidth Operations with Radio Droppables), a multi-layer networking solution which tightly co-designs the network architecture and high-level decision-making for improved comms. ACHORD provides bandwidth prioritization and timely and reliable data transfer despite intermittent connectivity. Furthermore, it exposes low-layer networking metrics to the application layer to enable robots to autonomously monitor, map, and extend the network via droppable radios, as well as restore connectivity to improve collaborative exploration. We evaluate our solution with respect to the comms performance in several challenging underground environments including the DARPA SubT Finals competition environment. Our findings support the use of data stratification and flow control to improve bandwidth-usage.Peer ReviewedPostprint (author's final draft

Communication, Coordination and Organization of Practical Robot Swarms

RÉSUMÉ : Depuis quelques années, les systèmes multi-robots gagnent rapidement en popularité dans une foule d’applications, telles que les drones de livraison d’Amazon et les flottes de voitures autonomes de Waymo. Ces systèmes doivent être déployés pendant des périodes prolongées avec un minimum de temps morts pour augmenter rentabilité. Le paradigme le plus établi pour la robotique dans l’industrie est la centralisation, où un seul ordinateur contrôle l’ensemble du système. Cependant, dans ces systèmes une simple faute, ou déconnexion, de l’ordinateur central peut conduire à des défaillances catastrophiques comme des drones tombant du ciel sur des passants. Ces défaillances soulignent l’importance des méthodes décentralisées pour les systèmes multi-robots qui répartissent la collecte de données et la prise de décision entre les robots. Malheureusement, l’utilisation de systèmes décentralisés peut conduire à un manque de connaissance de la situation globale au sein des robots individuels, en raison de la nature locale et dispersée des données. L’absence de méthodes appropriées pour transmettre les objectifs globaux aux robots entrave leur utilisation dans les applications réalistes, ce qui est le but ultime de ce travail. Les défis entourant le manque de connaissances globales dans les systèmes multi-robots distribués proviennent de l’absence d’outils de gestion de données appropriés pour stocker et gérer efficacement les données, ainsi que du besoin de mécanismes de consensus robustes. Les méthodes actuelles de stockage et de gestion des données dans les essaims de robots sont limitées à un seul état. En revanche, la quantité de données produites par les robots devient de plus en plus grande et importante au fur et à mesure de l’évolution des technologies de capteur (i.e., la densité de pixels qui augmente sans cesse dans les caméras). Les mécanismes actuels de partage des données disponibles dans les systèmes distribués et les réseaux pair à pair ne s’appliquent pas directement aux essaims de robots en raison de leur grande mobilité et de leurs ressources de calcul limitées. Dans ce travail, nous proposons donc SOUL, un mécanisme de partage de données pour le stockage et la gestion de gros blocs de données au sein de grands essaims de robots. SOUL crée un pool de mémoire commun à partir de la mémoire inutilisée des robots de l’essaim. Ce pool de mémoire commun est disponible pour les robots qui ont besoin de stockage supplémentaire. Nous montrons que SOUL n’a qu’un impact minimal sur la mobilité et qu’il s’adapte bien aux essaims de milliers de robots en simulation. Nous démontrons également l’utilisation de SOUL dans plusieurs applications pratiques sur un essaim de 10 robots physiques. ----------ABSTRACT Multi-robot systems are becoming more and more pervasive: clear examples are Amazon delivery drones and Waymo self-driving car fleets. These systems need to be deployed for extended periods with minimal downtime to increase profitability. The most established paradigm for robotics in industry is centralized, where a single computer controls the whole system. However, centralized systems have a single point of failure, which can lead to catastrophic failures like drones dropping from the sky on people. These failures stress the importance of decentralized methods for multi-robot systems, which distribute the data collection and decision-making among the robots. Unfortunately, the use of current state-of-the-art decentralized systems can lead to a lack of situational awareness within the robots, due to the local and scattered nature of the information. Communication issues can make coordination challenging, and the lack of proper methods to convey global goals to robots hinders their use in real-world applications, which is the ultimate goal of this work. The challenges surrounding the lack of situational awareness in distributed multi-robot systems stem from the lack of proper data management tools to store and manage data effectively, as well as robust consensus mechanisms. Common data-sharing mechanisms available in distributed systems and peer-to-peer networks do not apply directly to robot swarms due to their high mobility and limited computational resources. In this work we propose SOUL, a data-sharing mechanism for storing and managing large data blobs among large robot swarms. SOUL creates a common pool of memory from the unused memory of robots in the swarm. This common memory pool is available to robots that require additional storage. We show that SOUL has minimal impact on mobility and scales well to swarms of thousands of robots in simulations and also demonstrate the use of SOUL in several practical applications on a swarm of 10 physical robots

Energy Sufficiency in Unknown Environments via Control Barrier Functions

Maintaining energy sufficiency of a battery-powered robot system is a essential for long-term missions. This capability should be flexible enough to deal with different types of environment and a wide range of missions, while constantly guaranteeing that the robot does not run out of energy. In this work we present a framework based on Control Barrier Functions (CBFs) that provides an energy sufficiency layer that can be applied on top of any path planner and provides guarantees on the robot's energy consumption during mission execution. In practice, we smooth the output of a generic path planner using double sigmoid functions and then use CBFs to ensure energy sufficiency along the smoothed path, for robots described by single integrator and unicycle kinematics. We present results using a physics-based robot simulator, as well as with real robots with a full localization and mapping stack to show the validity of our approach