Transportation missions in aerospace are limited to the capability of each aerospace robot and the properties of the target transported object, such as mass, inertia, and grasping locations. We present a novel decentralized adaptive controller design for multiple robots that can be implemented in different kinds of aerospace robots. Our controller adapts to unknown objects in different gravity environments. We validate our method in an aerial scenario using multiple fully actuated hexarotors with grasping capabilities, and a space scenario using a group of space tugs. In both scenarios, the robots transport a payload cooperatively through desired three-dimensional trajectories. We show that our method can adapt to unexpected changes that include the loss of robots during the transportation mission.

Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable cooperative transport. However, allocating transportation tasks of objects with unknown weights remains challenging. Moreover, the presence of infeasible tasks (untransportable objects) can lead to robot stoppage (deadlock). This paper proposes a framework for dynamic task allocation that involves storing task experiences for each task in a scalable manner with respect to the number of robots. First, these experiences are broadcasted from the cloud server to the entire robot system. Subsequently, each robot learns the exclusion levels for each task based on those task experiences, enabling it to exclude infeasible tasks and reset its task priorities. Finally, individual transportation, cooperative transportation, and the temporary exclusion of tasks considered infeasible are achieved. The scalability and versatility of the proposed method were confirmed through numerical experiments with an increased number of robots and objects, including unlearned weight objects. The effectiveness of the temporary deadlock avoidance was also confirmed by introducing additional robots within an episode. The proposed method enables the implementation of task allocation strategies that are feasible for different numbers of robots and various transport tasks without prior consideration of feasibility.

One of the main problems that local authorities of large cities have to face is the regulation of urban mobility. They need to provide the means to allow for the efficient movement of people and distribution of goods. However, the provisioning of transportation services needs to take into account general global objectives, like reducing emissions and having more healthy living environments, which may not always be aligned with individual interests. Urban mobility is usually provided through a transport infrastructure that includes all the elements that support mobility. On many occasions, the capacity of the elements of this infrastructure is lower than the actual demand and thus different transportation activities compete for their use. In this paper, we argue that scarce transport infrastructure elements should be assigned dynamically and in a prioritised manner to transport activities that have a higher utility from the point of view of society; for example, activities that produce less pollution and provide more value to society. In this paper, we define a general model for prioritizing the use of a particular type of transportation infrastructure element called time-unlimited elements, whose usage time is unknown a priori, and illustrate its dynamics through two use cases: vehicle-specific dynamic access restriction in city centres (i) based on the usage levels of available parking spaces and (ii) to assure sustained admissible air quality levels in the city centre. We carry out several experiments using the SUMO traffic simulation tool to evaluate our proposal.

Abstract-- Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. Robots are versatile tools for object manipulation and In this paper, we present a practical multi-robot system transportation [1], and have a broad range of applications, along with a hierarchical navigation framework for the task including industry assembly lines [2], vehicle extraction [3], of transporting a series of luggage trolleys with autonomous and luggage collection at airports [4], [5], etc. Two nonholonomic robots that were previously used the movement of large objects requires the coordination of in our trolley collection work [5] are further adapted and multiple robots for enhanced strength or mobility.

Human-robot collaborative tasks foresee interactions between humans and robots with various degrees of complexity. Specifically, for tasks which involve physical contact among the agents, challenges arise in the modelling and control of such interaction. In this paper we propose a control architecture capable of ensuring a flexible and robustly stable physical human-robot interaction, focusing on a collaborative transportation task. The architecture is deployed onto a mobile manipulator, modelled as a whole-body structure, which aids the operator during the transportation of an unwieldy load. Thanks to passivity techniques, the controller adapts its interaction parameters online while preserving robust stability for the overall system, thus experimentally validating the architecture.

We study a human-robot collaborative transportation task in presence of obstacles. The task for each agent is to carry a rigid object to a common target position, while safely avoiding obstacles and satisfying the compliance and actuation constraints of the other agent. Human and robot do not share the local view of the environment. The human policy either assists the robot when they deem the robot actions safe based on their perception of the environment, or actively leads the task. Using estimated human inputs, the robot plans a trajectory for the transported object by solving a constrained finite time optimal control problem. Sensors on the robot measure the inputs applied by the human. The robot then appropriately applies a weighted combination of the human's applied and its own planned inputs, where the weights are chosen based on the robot's trust value on its estimates of the human's inputs. This allows for a dynamic leader-follower role adaptation of the robot throughout the task. Furthermore, under a low value of trust, if the robot approaches any obstacle potentially unknown to the human, it triggers a safe stopping policy, maintaining safety of the system and signaling a required change in the human's intent. With experimental results, we demonstrate the efficacy of the proposed approach.

Last month, we wrote about autonomous quadrotors from the University of Pennsylvania that use just a VGA camera and an IMU to navigate together in swarms. Without relying on external localization or GPS, quadrotors like these have much more potential to be real-world useful, since they can operate without expensive and complex infrastructure, even indoors. One potential application for drones like these is disaster operations, but honestly, that's just what everyone says when you ask them how their mobile robot could potentially be useful. What's much more interesting to us are commercial applications, and with drones, that inevitably means talking about delivery. There are a lot of reasons why we're skeptical about most commercial delivery drones, but that doesn't meant that the idea of using drones to move things from place to place isn't a good one. Vijay Kumar's lab at UPenn has been working on using their GPS-independent quadrotors for transporting payloads, and they're doing it collaboratively--the idea is that objects that are too large or heavy for one quadrotor to move can instead be moved by multiple quadrotors working together, and ultimately, they could be the best way to move items around a warehouse.

In heterogeneous multi-robot teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Yet platform-independence is desirable when planning actions for the various robots. We propose a platform-independent model of robot capabilities which we use as a planning domain. We extend existing planning techniques to support two requirements: generating new objects during planning; and, required concurrency of actions due to data flow which can be cyclic. The first requires online action instantiation, the second a small extension of the Planning Domain Definition Language (PDDL): allowing predicates in continuous effects. We evaluate the planner on benchmark domains and present results on an example object transportation task in simulation.