Team Of Heterogeneous Robots Research Articles

The Cooperation of Heterogeneous Mobile Robots in Manufacturing Environments using a Robotic Middleware Platform

The study of cooperation in heterogeneous robot teams has evolved due to the engineering and economic benefits attribute as well as the existence of diversities in homogeneous robot teams. The challenge of cooperation in these systems is a result of the task taxonomies and fundamental abilities of each robot in the team; there is therefore a need for an Artificial Intelligence (AI) system that processes these heterogeneities to facilitate robot cooperation. The research in this paper focuses on the cooperative teamwork behavior among heterogeneous mobile robots via the use of a robotic middleware platform. The contribution of this research is seen in the application of cooperative robotics in manufacturing processes where bottlenecks are relieved, resulting in the improvement of production efficiencies.

Towards heterogeneous robot team path planning in dynamic search and rescue scenarios

Towards heterogeneous robot team path planning in dynamic search and rescue scenarios

A robust approach to robot team learning

The paper achieves two outcomes. First, it summarizes previous work on concurrent Markov decision processes (CMDPs) currently demonstrated for use with multi-agent foraging problems. When using CMDPs, each agent models the environment using two Markov decision process (MDP). The two MDPs characterize a multi-agent foraging problem by modeling both a single-agent foraging problem, and multi-agent task allocation problem, for each agent. Second, the paper studies the effects of state uncertainty on a heterogeneous robot team that utilizes the aforementioned CMDP modelling approach. Furthermore, the paper presents a method to maintain performance despite state uncertainty. The resulting robust concurrent individual and social learning (RCISL) mechanism leads to an enhanced team learning behaviour despite state uncertainty. The paper analyzes the performance of the concurrent individual and social learning mechanism with and without a particle filter for a heterogeneous foraging scenario. The RCISL mechanism confers statistically significant performance improvements over the CISL mechanism.

Real-time WiFi localization of heterogeneous robot teams using an online random forest

In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest (ORF). The algorithm's robustness is increased by a Monte Carlo localization algorithm whose sensor model exploits the results of the ORF classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points' location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.

Coordination of Multiple Teams of Robots for an Optimal Global Plan

We consider multiple teams of heterogeneous robots, where each team is given a feasible task to complete in its workspace on its own, and where teams are allowed to transfer robots between each other. We study the problem of finding a coordination of robot transfers between teams to ensure an optimal global plan (with minimum makespan) so that all tasks can be completed as soon as possible by helping each other. We propose to solve this problem using answer set programming.

Marsupial teams of robots: deployment of miniature robots for swarm exploration under communication constraints

SUMMARYMobile Ad hoc Networks have attracted much attention in the last years, since they allow the coordination and cooperation between agents belonging to a multi-robot system. However, initially deploying autonomously a wireless sensor robot network in a real environment has not taken the proper attention. Moreover, maintaining the connectivity between agents in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. This paper compares two autonomous and realistic marsupial strategies for initial deployment in unknown scenarios, in the context of swarm exploration: Random and Extended Spiral of Theodorus. These are based on a hierarchical approach, in which exploring agents, named scouts, are autonomously deployed through explicit cooperation with supporting agents, denoted as rangers. Experimental results with a team of heterogeneous robots are conducted using both real and virtual robots. Results show the effectiveness of the methods, using a performance metric based on dispersion. Conclusions drawn in this work pave the way for a whole series of possible new approaches.

Performance of heterogeneous robot teams with personality adjusted learning

Abstract This paper presents a reinforcement learning algorithm, which is inspired by human team dynamics, for autonomous robotic multi agent applications. Individual agents on the team have heterogeneous capabilities and responsibilities. The learning algorithm assigns strictly local credit assignments to individual agents promoting scalability of the team size. The Personality Adjusted Learner (PAL) algorithm is applied to heterogeneous teams of robots with reward adjustments modified from earlier work on homogeneous teams and an information-based action personality type assignment algorithm has been incorporated. The PAL algorithm was tested in a robot combat scenario against both static and learning opponent teams. The PAL team studied included distinct commander, driver, and gunner agents for each robot. The personality preferences for each agent were varied systematically to uncover team performance sensitivities to agent personality preference assignments. The results show a significant sensitivity for the commander agent. This agent selected the robot strategy, and it was noted that the better performing commander personalities were linked to team oriented actions, rather than more selfish strategies. The driver and gunner agent performance remained insensitive to personality assignment. The driver and gunner actions did not apply at the strategic level, indicating that personality preferences may be important for agents responsible for learning to cooperate intentionally with teammates.

The Network Robot System: Enabling Social Human-Robot Interaction in Public Spaces

What kind of framework is needed to realize social robot applications in real public environments? Based on several years of experience in conducting field studies with social robots in public spaces, we identify key considerations that we have found to be indispensable in the development of robot services. We present a "network robot system" framework, which supports social robot services in five ways: observation of human behavior using environmental sensor networks, structured knowledge sharing, centralized resource and service allocation, global path planning for coordination between robots, and support for selected recognition and decision tasks by a human operator. We describe our network robot system implementation and present a demonstration in a shopping mall, illustrating how such a network robot system framework can be used to support heterogeneous teams of robots providing services in a real public environment.

Coordinating heterogeneous teams of robots using temporal symbolic planning

The efficient coordination of a team of heterogeneous robots is an important requirement for exploration, rescue, and disaster recovery missions. In this paper, we present a novel approach to target assignment for heterogeneous teams of robots. It goes beyond existing target assignment algorithms in that it explicitly takes symbolic actions into account. Such actions include the deployment and retrieval of other robots or manipulation tasks. Our method integrates a temporal planning approach with a traditional cost-based planner. The proposed approach was implemented and evaluated in two distinct settings. First, we coordinated teams of marsupial robots. Such robots are able to deploy and pickup smaller robots. Second, we simulated a disaster scenario where the task is to clear blockades and reach certain critical locations in the environment. A similar setting was also investigated using a team of real robots. The results show that our approach outperforms ad-hoc extensions of state-of-the-art cost-based coordination methods and that the approach is able to efficiently coordinate teams of heterogeneous robots and to consider symbolic actions.

Heterogeneous Context-Aware Robots Providing a Personalized Building Tour

Existing robot guides offer a tour of a building, such as a museum or science centre, to one or more visitors. Usually the tours are predefined and lack support for dynamic interactions between the different robots. This paper focuses on the distributed collaboration of multiple heterogeneous robots (receptionist, companion) guiding visitors through a building. Semantic techniques support the formal definition of tour topics, the available content on a specific topic, and the robot and person profiles including interests and acquired knowledge. The robot guides select topics depending on their participants' interests and prior knowledge. Whenever one guide moves into the proximity of another, the guides automatically exchange participants, optimizing the amount of interesting topics. Robot collaboration is realized through the development of a software module that allows a robot to transparently include behaviours performed by other robots into its own set of behaviours. The multi-robot visitor guide application is integrated into an extended distributed heterogeneous robot team, using a receptionist robot that was not originally designed to cooperate with the guides. Evaluation of the implemented algorithms presents a 90% content coverage of relevant topics for the participants.

Specialization analysis of embodied evolution for robotic collective tasks

The objective of this work is to analyze embodied evolution based algorithms in coordinated multi-robot tasks that require specialization. This type of algorithm performs a Darwinian open-ended evolution where the individuals that make up the population are embodied in the physical robots and situated in an environment. The robots interact autonomously in an asynchronous fashion, leading to a complex dynamic system in continuous evolution with dependencies among parameters that make theoretical studies of specialization quite difficult in real cases. Consequently, the aim here is to perform a theoretical analysis of this type of embodied evolution based algorithms, establishing a set of canonical parameters that define their operation. A generic algorithm of this type is designed that allows us to formally study the relevance of the canonical parameters. In this paper this study concentrates on specialization for the construction of heterogeneous robotic teams. The conclusions obtained in the theoretical framework are confirmed in a real multi-robot collective gathering task using one of the many real embodied evolution based algorithms and showing that two canonical parameters are the most relevant in terms of specialization for this type of algorithms. Some insights into how to adjust these canonical parameters in a real problem are provided.

Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction

This article considers the problem of optimal task allocation for heterogeneous teams, for example, teams of heterogeneous robots or human-robot teams. It is well-known that this problem is NP-hard and hence computationally feasible approaches must develop an approximate solution. Here, we propose a solution via a Stochastic Clustering Auction (SCA) that uses a Markov chain search process along with simulated annealing. This is the first stochastic auction method used in conjunction with global optimization. It is based on stochastic transfer and swap moves between the clusters of tasks assigned to the various robots and considers not only downhill movements, but also uphill movements, which can avoid local minima. A novel feature of this algorithm is that, by tuning the annealing suite and turning the uphill movements on and off, the global team performance after algorithm convergence can slide in the region between the global optimal performance and the performance associated with a random allocation. Extensive numerical experiments are used to evaluate the performance of SCA in terms of costs and computational and communication requirements. For centralized auctioning, the SCA algorithm is compared to fast greedy auction algorithms. Distributed auctioning is then compared with centralized SCA.

Multiple Objective Robot Coalition Formation

Abstract In multiple robot systems, the problem of allocation of complex tasks to heterogeneous teams of robots, also known as the multiple robot coalition formation problem, has begun to receive considerable attention. Efforts to address the problem range from heuristics based approaches that search the subspaces of the coalition structure to evolutionary learning approaches. Conventional approaches typically strive to optimize a single objective function such as the number of tasks executed or the time required to execute all tasks, or a weighted function of such objectives. In real world applications, objectives such as minimizing distance traveled and maximizing the number of tasks completed are often conflicting in nature. The coalition formation problem thus naturally lends itself to a multi-objective optimization approach based on evolutionary learning. In this paper, we formulate the problem of mapping coalitions of robots to a set of tasks as a multi-objective optimization problem and propose a variant of non-dominated sorting genetic algorithm (NSGA-II) to arrive at trade-off solutions. Additionally, we extend the solution to domains where robot resources are non-additive. Simulations demonstrate the efficacy of the proposed approach in generating the set of Pareto-optimal solutions.

Multirobot Active Target Tracking With Combinations of Relative Observations

In this paper, we study the problem of optimal trajectory generation for a team of heterogeneous robots moving in a plane and tracking a moving target by processing relative observations, i.e., distance and/or bearing. Contrary to previous approaches, we explicitly consider limits on the robots' speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single tracking sensor and seek the next sensing location in order to minimize the uncertainty about the target's position. We show that although the corresponding optimization problem involves a nonconvex objective function and a nonconvex constraint, its global optimal solution can be determined analytically. We then extend the approach to the case of multiple sensors and propose an iterative algorithm, i.e., the Gauss-Seidel relaxation (GSR), to determine the next best sensing location for each sensor. Extensive simulation results demonstrate that the GSR algorithm, whose computational complexity is linear in the number of sensors, achieves higher tracking accuracy than gradient descent methods and has performance that is indistinguishable from that of a grid-based exhaustive search, whose cost is exponential in the number of sensors. Finally, through experiments, we demonstrate that the proposed GSR algorithm is robust and applicable to real systems.

이종 로봇팀의 협업을 통한 맵 빌딩과 위치추정

In this paper we present cooperation of heterogeneous robot team, composed of a wheeled robot and a helicopter for localization and map building. This heterogeneous robot team can successfully fulfill task by combining the abilities of both robots than single robot because wheeled robot and helicopter have complementing ability. The scenario describes a tightly cooperative task, where the wheeled robot move carrying the helicopter and detect obstacles, if there are obstacles, helicopter take off for map building and land, then robot team move destination avoiding obstacles. We present PID controller for position control of helicopter and transformation algorithm to global coordinate from image pixel coordinate. Experimental result show that the proposed method is valid.

LUNARES: lunar crater exploration with heterogeneous multi robot systems

The LUNARES (Lunar Crater Exploration Scenario) project emulates the retrieval of a scientific sample from within a permanently shadowed lunar crater by means of a heterogeneous robotic system. For the accomplished earth demonstration scenario, the Shakelton crater at the lunar south pole is taken as reference. In the areas of permanent darkness within this crater, samples of scientific interest are expected. For accomplishment of such kind of mission, an approach of a heterogeneous robotic team consisting of a wheeled rover, a legged scout as well as a robotic arm mounted on the landing unit was chosen. All robots act as a team to reach the mission goal. To prove the feasibility of the chosen approach, an artificial lunar crater environment has been established to test and demonstrate the capabilities of the robotic systems. Figure 1 depicts the systems in the artificial crater environment. For LUNARES, preexisting robots were used and modified were needed in order to integrate all subsystems into a common system control. A ground control station has been developed considering conditions of a real mission, requiring information of autonomous task execution and remote controlled operations to be displayed for human operators. The project successfully finished at the end of 2009. This paper reviews the achievements and lessons learned during the project.

Distributed Service-Based Cooperation in Aerial/Ground Robot Teams Applied to Fire Detection and Extinguishing Missions

This paper presents a system for the coordination of aerial and ground robots for applications such as surveillance and intervention in emergency management. The overall system architecture is described. An important part for the coordination between robots is the task allocation strategy. A distributed market-based algorithm, called S + T, has been developed to solve the multi-robot task allocation problem in applications that require cooperation among the robots to accomplish all the tasks. Using this algorithm, robots can provide transport and communication relay services dynamically to other robots during the missions. Moreover, the paper presents a demonstration with a team of heterogeneous robots (aerial and ground) cooperating in a mission of fire detection and extinguishing.

Optimization problems involving collections of dependent objects

We describe a class of problems motivated by numerous real-world applications where there is a collection of objects that have both a cost and a value, but where some of those objects depend upon other objects to obtain their full value. Applications include finding an optimal order for transferring files under threat of system failure, ordering sequences of actions by a heterogeneous team of agents or robots, picking an optimal set of products to store in a warehouse, selecting courses to take at a university, or picking what products to cut from production. We formalize the problem of representing objects and their dependence relationships as a directed acyclic graph (DAG). We define simple formulae for calculating the utility of both sets and sequences of graph vertices. We motivate, using real-world examples, a taxonomy of problems associated with the model we present. We also prove that two variants of problems associated with our formalism are NP-hard, and present an efficient algorithm for solving a restricted version of a third problem.

Data fusion in automated robotic inspection systems

Teams of small modular inspection vehicles for automated inspection tasks offer the possibility of employing a variety of different NDE inspection methods simultaneously. By synergistically utilising information derived from multiple sources, individual deficiencies and limitations can be partially compensated, leading to a more accurate and precise evaluation of the condition of the engineering structure under test. This paper presents approaches based on fusion of NDE data that have been obtained by a heterogeneous team of small inspection robots which are equipped with payloads for magnetic flux leakage, eddy current and ultrasonic inspection. Any potential uncertainties in individual measurements regarding the location of defects constitute the basis for fusion methods based on statistical and probabilistic algorithms. Images of a two-dimensional test structure have been constructed from data derived from different scans, indicating the positions of detected artificial defects. Applying the Dempster-Shqfer theory of evidence and Bayesian analysis, the confidence level in the accuracy of these images is increased and the uncertainty reduced.

Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup rescue

AbstractUrban Search and Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi‐robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real‐world validation results on the engine and algorithms are offered. © 2007 Wiley Periodicals, Inc.