Multi-robot Application Research Articles

It Costs to Get Costs! A Heuristic-Based Scalable Goal Assignment Algorithm for Multi-Robot Systems

The goal assignment problem for a multi-robot application involves assigning a unique goal to each robot to minimize the total cost of movement for all the robots. A significant step in the state-of-the-art algorithms solving this problem is to find the cost associated with each robot-goal pair. For a large multi-robot system with many robots and many goals in a complex workspace, the computation time required to find the paths for all robot-goal pairs may become prohibitively large. We present an algorithm that solves the optimal goal assignment problem without computing the paths between all the robot-goal pairs. Instead, our algorithm computes the obstacle-free optimal path for any robot-goal pair in a demand-driven way, i.e., if it is absolutely required to ensure the optimality of the goal assignment. We evaluate our algorithm extensively on both randomly generated and standard workspaces for hundreds of robots. Our experimental results demonstrate that the proposed algorithm achieves an order-of-magnitude speedup over the state-of-the-art baseline algorithm. To the best of our knowledge, our algorithm is the first one to solve the multi-robot optimal goal assignment problem without computing the paths for all robot-goal pairs explicitly, guaranteeing high scalability.

Petri Net Toolbox for Multi-Robot Planning under Uncertainty

Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorithms. It implements a state-of-the-art approach to representing multi-robot systems: generalized Petri nets with rewards (GSPNRs). GSPNRs enable capturing multiple robots, decision states, action execution states and respective outcomes, action duration uncertainty, and team-level objectives. We introduce a novel algorithm that simplifies the model design process as it generates a GSPNR from a topological map. We also introduce a novel execution algorithm that coordinates the multi-robot system according to a given policy. This is achieved without compromising the model compactness introduced by representing robots as indistinguishable tokens. We characterize the computational performance of the toolbox with a series of stress tests. These tests reveal a lightweight implementation that requires low CPU and memory usage. We showcase the toolbox functionalities by solving a multi-robot inspection application, where we extend GSPNRs to enable the representation of heterogeneous systems and system resources such as battery levels and counters.

UWB-Based Localization for Multi-UAV Systems and Collaborative Heterogeneous Multi-Robot Systems

Ultra-wideband technology has emerged in recent years as a robust solution for localization in GNSS denied environments. In particular, its high accuracy when compared to other wireless localization solutions is enabling a wider range of collaborative and multi-robot application scenarios, being able to replace more complex and expensive motion-capture areas for use cases where accuracy in the order of tens of centimeters is sufficient. We present the first survey of UWB-based localization focused on multi-UAV systems and heterogeneous multi-robot systems. We have found that previous literature reviews do not consider in-depth the challenges in both aerial navigation and navigation with multiple robots, but also in terms of heterogeneous multi-robot systems. In particular, this is, to the best of our knowledge, the first survey to review recent advances in UWB-based (i) methods that enable ad-hoc and dynamic deployments; (ii) collaborative localization techniques; and (iii) cooperative sensing and cooperative maneuvers such as UAV docking on mobile platforms. Finally, we also review existing datasets and discuss the potential of this technology for both localization in GNSS-denied environments and collaboration in multi-robot systems.

Cooperative Robots to Observe Moving Targets: Review.

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization

This paper investigates a decentralized trajectory optimization method to solve a nonlinear constrained trajectory optimization problem. Especially, we consider a problem constrained on the terminal time and angle in a multi-robot application. The proposed algorithm is based on virtual motion camouflage (VMC) and particle swarm optimization (PSO). VMC changes a typical full space optimal problem to a subspace optimal problem, so it can reduce the dimension of the original problem by using path control parameters (PCPs). If PCPs are optimized, then the optimal path can be obtained. In this work, PSO is used to optimize these PCPs. In multi-robot path planning, each robot generates its own optimal path by using VMC and PSO, and sends its path information to the other robots. Then, the other robots use this path information when planning their own paths. Simulation and experimental results show that the optimal paths considering the terminal time and angle constraints are effectively generated by decentralized VMC and PSO.

Trajectory Planning and Control for Airport Snow Sweeping by Autonomous Formations of Ploughs

This article presents a control approach that enables an autonomous operation of fleets of unmanned snow ploughs at large airports. The proposed method is suited for the special demands of tasks of the airport snow shovelling. The robots have to keep a compact formation of variable shapes during moving into the locations of their deployment and for the autonomous sweeping of runways surfaces. These tasks are solved in two independent modes of the airport snow shoveling. The moving and the sweeping modes provide a full-scale solution of the trajectory planning and coordination of vehicles applicable in the specific airport environment. Nevertheless, they are suited for any multi-robot application that requires complex manoeuvres of compact formations in dynamic environment. The approach encapsulates the dynamic trajectory planning and the control of the entire formation into one merged optimization process via a novel Model Predictive Control (MPC) based methodology. The obtained solution of the optimization includes a complete plan for the formation. It respects the overall structure of the workspace and actual control inputs for each vehicle to ensure collision avoidance and coordination of team members. The presented method enables to autonomously design arbitrary manoeuvres, like reverse driving or turning of compact formations of car-like robots, which frequently occur in the airport sweeping application. Examples of such scenarios verifying the performance of the approach are shown in simulations and hardware experiments in this article. Furthermore, the requirements that guarantee a convergence of the group to a desired state are formulated for the formation acting in the sweeping and moving modes.

Hierarchy Organization Model Based on the Logic of Tasks

Hierarchy organization is suitable for multi-robot system application. Robot or collaborative robots group is considered as an agent, then the organization model consists of agents and the relations between them. This paper introduces the logic of tasks combined with description logic to construct logic system. According to related works [4], we can prove is decidable, complete and sound. Based on the logic system, author presented a formal task-oriented hierarchy organization model that can deduce a plan with a strategy can complete task or not.

Designing a meta-model for a generic robotic agent system using Gaia methodology

The emergence of multi-agent systems in the past years has led to the development of new methodologies to assist in the requirements and architectural analysis, as well as in the design phases of such systems. Consequently, several Agent Oriented Software Engineering (AOSE) methodologies have been proposed. In this paper, we analyze some AOSE methodologies, including Gaia, which supports the architectural design stage, and some proposed extensions. We then use an adapted version of this methodology to design an abstract generic system meta-model for a multi-robot application, which can be used as a basis for the design of these systems, avoiding or shortening repetitive tasks common to most systems. Based on the proposed Generic Robotic Agent Meta-Model (GRAMM), two distinct models for two different applications are derived, demonstrating the versatility and adaptability of the meta-model. By adapting the Gaia methodology to the design of open systems, this work makes the designers’ job faster and easier, decreasing the time needed to complete several tasks, while at the same time maintaining a high-level overview of the system.

Multi-Robot Approach for Automation of an Industrial Profile Lamination Process

In this paper we present a multi-robot application from the field of industrial automation. The multi-robot system forms the central part of a novel kind of profile wrapping machine, which is used to laminate decorative surfaces onto profiles, e.g. parts for window frames. The robots operate in a confined area and perform different tasks such as re-tooling and setting up machine parts simultaneously as quick and as accurate as possible. During lamination, each robot uses a special roller to press the adhesive-coated surface onto the profile. For this application, we use a scalable number of at least 10 and up to 50 standard articulated-arm robots. The system consists of three lines of robots, three movable depots capable of holding hundreds of different rollers and a computer with a fieldbus interface allowing for communication with additional machine components like servo converters. On the computer runs custom software and serves as a central control unit. The control software receives the given reference values, like roller positions and desired pressure, and distributes them to the robots. Robot actions are triggered, monitored and synchronized by the central control unit, but every robot performs its tasks autonomously. The main control unit and robots communicate via standard Ethernet, since time-critical actions can be handled on each robot's own controller. User interface and sophisticated software modules allowing for a completely automated machine configuration are also linked to the control system via Ethernet, as well as any additional components like data logging or remote service applications. Tests revealed that the multi-robot approach for profile wrapping reduces set-up time from more than 1.5hrs on conventional machines to approximately five minutes. At the same time, it ensures a higher and more constant process quality as the system autonomously compensates for wear of rollers or variation in the profile's geometry.

ROBUST 3-D OBJECT DETECTION AND FEATURE EXTRACTION FOR MULTI-ROBOT COOPERATION

This paper develops a computer vision system for multi-robot cooperation in rescue operations. The work is focused on providing robust and fast vision capabilities to the robots to meet the expected level of performance. Enhanced versions of existing techniques and new techniques are utilized to develop an adaptive vision system architecture for use in an unstructured environment of multi-robot activity in an emergency scenario. Different types of object detection methods are selected in real time in the developed system according to the requirements of a robot. To validate the developed system for use in a multi-robot application, rigorous experiments are conducted in a typical unstructured environment. Features such as invariance of scale, rotation, illumination, and occlusion are tested with different types of objects, for various methods. Generally, good results are obtained.

A study of mechanisms for improving robotic group performance

Many collaborative multi-robot application domains have limited areas of operation that cause spatial conflicts between robotic teammates. These spatial conflicts can cause the team's productivity to drop with the addition of robots. This phenomenon is impacted by the coordination methods used by the team-members, as different coordination methods yield radically different productivity results. However, selecting the best coordination method to be used by teammates is a formidable task. This paper presents techniques for creating adaptive coordination methods to address this challenge. We first present a combined coordination cost measure, CCC, to quantify the cost of group interactions. Our measure is useful for facilitating comparison between coordination methods, even when multiple cost factors are considered. We consistently find that as CCC values grow, group productivity falls. Using the CCC, we create adaptive coordination techniques that are able to dynamically adjust the efforts spent on coordination to match the number of perceived coordination conflicts in a group. We present two adaptation heuristics that are completely distributed and require no communication between robots. Using these heuristics, robots independently estimate their combined coordination cost (CCC), adjust their coordination methods to minimize it, and increase group productivity. We use simulated robots to perform thousands of experiment trials to demonstrate the efficacy of our approach. We show that using adaptive coordination methods create a statistically significant improvement in productivity over static methods, regardless of the group size.