Heterogeneous Multi-robot Teams Research Articles

Scalable, Pairwise Collaborations in Heterogeneous Multi-Robot Teams

Scalable, Pairwise Collaborations in Heterogeneous Multi-Robot Teams

Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.

Agile Fixed-Wing UAVs for Urban Swarm Operations

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. Such attributes make these vehicles ideally suited for long-range or high-speed reconnaissance operations and position them as valuable complementary members of a heterogeneous multi-robot team. However, these vehicles have traditionally been severely limited with regards to both vertical take-off and landing (VTOL) as well as maneuverability, which greatly restricts their utility in environments characterized by complex obstacle fields (e.g., forests or urban centers). This paper describes a set of algorithms and hardware advancements that enable agile fixed-wing UAVs to operate as members of a swarm in complex urban environments. At the core of our approach is a direct nonlinear model predictive control (NMPC) algorithm that is capable of controlling fixed-wing UAVs through aggressive post-stall maneuvers. We demonstrate in hardware how our online planning and control technique can enable navigation through tight corridors and in close proximity to obstacles.We also demonstrate how our approach can be combined with onboard stereo vision to enable high-speed flight in unknown environments. Finally, we describe our method for achieving swarm system integration; this includes a gimballed propeller design to facilitate automatic take-off, a precision deep-stall landing capability, multi-vehicle collision avoidance, and software integration with an existing swarm architecture.

Comparing Complementary Kalman Filters Against SLAM for Collaborative Localization of Heterogeneous Multirobot Teams

Abstract This paper presents an investigation of collaborative localization for heterogeneous robots, resulting in a scheme for relative localization of a heterogeneous team of low-cost mobile robots. A novel complementary Kalman filter (CKF) approach is presented to address collaborative localization and mapping by optimally estimating the error states of the team. This indirect filter optimally combines the inertial/visual proprioceptive measurements of each vehicle with stereoscopic measurements made by unmanned ground vehicles (UGVs). An analysis is presented for both CKF and simultaneous localization and mapping (SLAM) approaches on maps containing randomly placed obstacles. In both simulation and experiments, we demonstrate the proposed methodology with a heterogeneous robot team. Six behavioral strategies, specifying the role and behavior of each robot, are simulated and evaluated for both CKF and SLAM approaches on maps containing randomly placed obstacles. Results show that the sources of error, image quantization, asynchronous sensors, and a nonstationary bias were sufficiently modeled to estimate the pose of the aerial robot. The results demonstrate localization accuracies of 2–4 cm, on average, while the robots operate at a distance from each other between 1 m and 4 m. The best performing behavior for the CKF approach maintained an average positional error of 2.2 cm and a relative error of 0.30% of the distance traveled for the entire team at the conclusion of maneuvers. For all multi-UGV strategies, the CKF approach outperformed the best SLAM results by a 6.7 cm mean error (0.48% of distance traveled).

Resilient Monitoring in Heterogeneous Multi-Robot Systems Through Network Reconfiguration

We propose a framework for resilience in a networked heterogeneous multirobot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors, which are identified based on the team’s communication graph. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable ( e.g., a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually estimate the state of an exogenous process. We encode the end-to-end effect of a robot’s resource loss on the monitoring performance of the team by defining a new stronger notion of observability— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-hop observability</i> . By abstracting the impact that low-level individual resources have on the task performance through the notion of one-hop observability, our framework leads to the principled reconfiguration of information flow in the team to effectively replace the lost resource on one robot with information from another, as long as certain conditions are met. Network reconfiguration is converted to the problem of selecting edges to be modified in the system’s communication graph after a resource failure has occurred. A controller based on finite-time convergence control barrier functions drives each robot to a spatial location that enables the communication links of the modified graph. We validate the effectiveness of our framework by deploying it on a team of differential-drive robots estimating the position of a group of quadrotors.

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multirobot Systems

In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Toward this end, the task prioritization and execution—through which the allocation of tasks to robots is effectively realized—are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations, where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

Decentralized Ability-Aware Adaptive Control for Multi-Robot Collaborative Manipulation

Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this letter, a Decentralized Ability-Aware Adaptive Control (DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C) is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot's force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object's reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.

Autonomous VTOL-UAV Docking System for Heterogeneous Multirobot Team

Heterogeneous robot teams offer significant advantages for exploration and navigation missions in unknown environments. Notably, the air-ground robot teams have superior efficiency due to their mixed abilities and shared sensor resources useful when completing missions. However, the high energy consumption and reduced payload capability of the vertical take-off and landing (VTOL) aerial platforms limit the complete robot team's behavior. At some point in a mission, the aircraft needs to return to the base, land, and replenish its batteries. This task restricts the team to explore the environment in-depth and to work for longer durations. To solve this problem, we present an autonomous system for docking a VTOL-unmanned aerial vehicles (UAVs) with a mobile manipulator. The central measurement-actuation unit consists of a robot manipulator mounted on a mobile platform with a visual sensor configured as an eye-in-hand device. The visual information is used to execute a stable UAV tracking to achieve air-ground robot contact firmly. This strategy allows the multirobot team to economize and potentially recover aircraft energy. It also enables the robust ground platform to protect and store the aircraft facilitating to continue the mission for a longer duration. The on-site autonomous docking can be initiated multiple times, allowing the team to explore larger areas and complete long-term missions. The proposed method has been tested in simulations and real environments with detailed experimental results.

A Routing Framework for Heterogeneous Multi-Robot Teams in Exploration Tasks

This letter proposes a routing framework for heterogeneous multi-robot teams in exploration tasks. The proposed framework deals with a combinatorial optimization problem, and provides a new solving algorithm, for Generalized Team Orienteering Problem (GTOP). In this letter, a route optimization problem is formulated for a heterogeneous multi-robot system. A novel problem solver is also proposed based on self-organizing map. The proposed framework has a strong advantage in its scalability because the processing time is independent from the number of robots, and the heterogeneity of the team. The validity of the proposed framework is evaluated in the exploration, and mapping tasks by heterogeneous robot team with overlapping abilities. The simulation results show the effectiveness of the proposed framework, and how it outperforms the conventional greedy exploration scheme.

Planning Paths for Package Delivery in Heterogeneous Multirobot Teams

This paper addresses the task scheduling and path planning problem for a team of cooperating vehicles performing autonomous deliveries in urban environments. The cooperating team comprises two vehicles with complementary capabilities, a truck restricted to travel along a street network, and a quadrotor micro-aerial vehicle of capacity one that can be deployed from the truck to perform deliveries. The problem is formulated as an optimal path planning problem on a graph and the goal is to find the shortest cooperative route enabling the quadrotor to deliver items at all requested locations. The problem is shown to be NP-hard. A solution is then proposed using a novel reduction to the Generalized Traveling Salesman Problem, for which well-established heuristic solvers exist. The heterogeneous delivery problem contains as a special case the problem of scheduling deliveries from multiple static warehouses. We propose two additional algorithms, based on enumeration and a reduction to the traveling salesman problem, for this special case. Simulation results compare the performance of the presented algorithms and demonstrate examples of delivery route computations over real urban street maps.

A Framework for Task Planning in Heterogeneous Multi Robot Systems Based on Robot Capabilities

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.

Scalable Task Assignment for Heterogeneous Multi-Robot Teams

This work deals with the development of a dynamic task assignment strategy for heterogeneous multi-robot teams in typical real world scenarios. The strategy must be efficiently scalable to support problems of increasing complexity with minimum designer intervention. To this end, we have selected a very simple auction-based strategy, which has been implemented and analysed in a multi-robot cleaning problem that requires strong coordination and dynamic complex subtask organization. We will show that the selection of a simple auction strategy provides a linear computational cost increase with the number of robots that make up the team and allows the solving of highly complex assignment problems in dynamic conditions by means of a hierarchical sub-auction policy. To coordinate and control the team, a layered behaviour-based architecture has been applied that allows the reusing of the auction-based strategy to achieve different coordination levels.

Analysis of autonomous cooperative assembly using coordination schemes by heterogeneous robots using a control basis approach

Robotic technology is quickly evolving allowing robots to perform more complex tasks in less structured environments with more flexibility and autonomy. Heterogeneous multi-robot teams are more common as the specialized abilities of individual robots are used in concert to achieve tasks more effectively and efficiently. An important area of research is the use of robot teams to perform modular assemblies. To this end, this paper analyzed the relative performance of two robots with different morphologies and attributes in performing an assembly task autonomously under different coordination schemes using force sensing through a control basis approach. A rigid, point-to-point manipulator and a dual-armed pneumatically actuated humanoid robot performed the assembly of parts under a traditional "push-hold" coordination scheme and a human-mimicked "push-push" scheme. The study revealed that the scheme with higher level of cooperation--the "push-push" scheme--performed assemblies faster and more reliably, lowering the likelihood of stiction phenomena, jamming, and wedging. The study also revealed that in "push-hold" schemes industrial robots are better pushers and compliant robots are better holders. The results of our study affirm the use of heterogeneous robots to perform hard-to-do assemblies and also encourage humans to function as holder's when working in concert with a robot assistant for insertion tasks.

Formation Control of Heterogeneous Multi-Robot Systems

In this paper, a new position feedback based formation control method for heterogeneous multi-robot teams is presented and evaluated. The formation behaviors are integrated with dynamic reference object based collaborative navigation and efficient obstacle avoidance to maintain and change formation real-time. This method is computationally efficient and easy to coordinate in heterogeneous systems. The time to formalize and switch specified formation patterns can be controlled by adjusting the position feedback parameter. Satisfactory experimental results are obtained in simulation and real heterogeneous multi-robot system which consists of autonomous vehicles and legged robots.

ROBOT AIRSHIP FOR EDUCATION AND RESEARCH – MODELLING AND CONTROL

At the University of Siegen a robot airship for cooperation with a heterogeneous multi-robot team is built to perform search and rescue as well as exploration missions. The airship control is based on an aerodynamic model derived from real airship models. The airship is able to fly autonomously over the operation field and is sharing sensor information with the ground based robots and a tele-operator.

Mobile Robots and Airship in a Multi-Robot Team

At the University of Siegen a heterogeneous multi-robot team is created to perform search and rescue or exploration missions. The robot team consists of ground based robots equipped with inertial sensors and 3D vision. The team is assisted by a robot airship flying over the operation field and sharing information with the ground based robots. All robots use a uniform user interface for tele control and additionallysupply force feedback manual operator control for human interaction.

Lifelong Adaptation in Heterogeneous Multi-Robot Teams: Response to Continual Variation in Individual Robot Performance

Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment.

Adaptive heterogeneous multi-robot teams

This research addresses the problem of achieving fault-tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. We describe a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since such cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, we describe in detail our experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes in the capabilities of the robot team.