In this paper we consider a team of robots that are tasked with tracking a moving target cooperatively, while maintaining their connectivity to a base station and avoiding collision. We propose a novel extension of the classical navigation function framework in order to ensure task completion. More specifically, we modify the classical definition of the navigation functions to (1) incorporate measures of link qualities and (2) include the impact of a time-varying objective. Our proposed communication-aware navigation function framework is aimed at maintaining robot connectivity in realistic communication environments, while avoiding collision with both fixed and moving obstacles. We consider both packet-dropping and communication noise based receivers. We furthermore prove the convergence of the proposed framework under certain conditions. Finally, our simulation results show the performance of the proposed navigation framework.
Humans and robots each have strengths and weaknesses associated with making good decisions to address complex tasks in uncertain, changing environments. We investigate how humans and robots can best jointly contribute to decision making so that strengths are exploited and weaknesses compensated. Our approach to this integration problem is to leverage experimental and modeling work of psychologists on human decision making. We seek commonality between the kinds of decisions humans make in complex tasks and the kinds of decisions humans make in psychology experiments; when commonality conditions are met, the psychology results can be used to predict how humans will behave in the complex task. A problem well studied in the psychology literature is the two-alternative forced-choice task, in which the human subject chooses between two options at regular time intervals and receives a reward after each choice. Interestingly, experiments show convergence of the aggregate behavior to rewards that are often suboptimal. We introduce a decision-making problem associated with a complex task that integrates human and robotic decision-making dynamics with feedback. The setting is a human-supervised collective robotic foraging problem, where the human decision-making takes the form of a two-alternative forced-choice task and the reward report is a feedback from the robots. Using a popular, experimentally verified, decision-making model, we prove convergence of the human behavior to the observed aggregate decision making for reward structures with matching points. Since behavior converges to suboptimal performance, we show how adaptive laws for the robot feedback, which use only local information, can be applied to help the human make optimal decisions.
In this paper we study robustness of aggregation in networks of coupled identical systems driven by possibly different external inputs. This property is guaranteed by providing a finite L2 gain condition for the closed-loop system. We show, for a class of systems, how robustness depends on the connectivity of the underlying communication graph. Applications range from coordination problems where there are conflicting objectives to the study of aggregation phenomena where perturbations of the nominal systems must be taken into account. Both scenarios arise in networks of biological and engineered coordinating systems.
We address the effects of the aquatic communication channel constraints on the control performances of marine robot teams. The aquatic acoustic channels suffer from significant frequency and distance dependent attenuation, extensive time-varying multipath, motion-induced Doppler distortion and extreme channel latency due to the low speed of sound. Therefore, the available bandwidth is strongly limited and distance dependent. Due to all these limitations, the introduction of intra-vehicle exchanged information in the control loop of marine robots can degrade the overall system performance. We give an overview of the communication needs in the control of a marine robotic team. A realistic model of the aquatic communication channel is considered, and different limitations of the channel are addressed, e.g., message error rate and communication delays. An overview of the experimental test-bed that is used to study the communication characteristics of the aquatic channel is presented. Finally, we report on an extensive simulation study on the performance of a controller for a marine surface vessel which relies on an acoustic communication channel for information sensed at a distance.
The problem of optimizing the position of a mobile fusion center in a sensor network is considered. The optimization criterion of interest is the sum distortion for the communication of all the information from each of the nodes to the fusion center. Transmission losses along underwater links are modeled and a time-multiplexing architecture imposed on the sensor nodes for communication with the fusion center. Classical results from the information theory literature are leveraged and the optimization problem is formulated and solved analytically for the case when the fusion center is at one fixed location and numerically for the case when the fusion center is mobile. It is observed that in the low node power regime, with the fusion center at a fixed location, the fusion center selectively communicates with a few nodes while turning the others off. In the high power regime, the time allocated to the nodes is a function of the information they need to transmit to the destination and the distance from the node to the fusion center. Further, when the fusion center is mobile, in the low power regime, the fusion center is placed close to the node with the largest information content while for higher powers the difference from a fixed fusion center declines.
We present a novel design for connecting pieces of a computationally enhanced construction kit, an optocoupled ball and socket joint. Unlike other existing connectors, our joint is poseable and allows both the topology and dynamic configuration of a kit assembly to be sensed. We provide a survey of existing computationally enhanced construction kit connectors and describe the technical details of our design.
This paper presents a new methodology for the control and design of distributed service architectures in an open environment. A domotic service illustrates and introduces the modelling keypoints. In particular, an explicit modelling of the attentional mechanism is used to overcome the lack of a global state in distributed systems and the relative impossibility to explicitly model all external events.
This paper presents an effective scheme to improve position estimation accuracy for multiple robot localization under outdoors. This scheme is applicable to cooperative localization (CL) system in which a robot exchanges no more relative position information between any of the robots. Applying the relative distance acquired by considering the error correlation of global positioning system (GPS) to cooperative localization framework, we can increase the positioning accuracy more than the CL system under certain circumstances without detecting other robot in a group. We consider a group of three robots equipped proprioceptive sensors such as odometer and exteroceptive sensors such as GPS and digital magnetic compass (DMC). In addition, each robot in the group is able to share the position data from sensors with others through wireless communication device. We prove to be strong error correlation between two robots' GPS position data acquired from experiment. Finally, in order to show the validity of our scheme, the proposed algorithm is implemented for multi-robot localization using experimental data of GPS error correlation.
In this paper, we consider the design and implementation of practical pursuit-evasion games with networked robots, where a communication network provides sensing-at-a-distance as well as a communication backbone that enables tighter coordination between pursuers. We first develop, using the theory of zero-sum games, an algorithm that computes the minimal completion time strategy for pursuit-evasion when pursuers and evaders have same speed, and when all players make optimal decisions based on complete knowledge. Then, we extend this algorithm to when evader are significantly faster than pursuers. Unfortunately, these algorithms do not scale beyond a small number of robots. To overcome this problem, we design and implement a partition algorithm where pursuers capture evaders by decomposing the game into multiple multi-pursuer single-evader games. We show that the partition algorithm terminates, has bounded capture time, is robust, and is scalable in the number of robots. We then describe the design of a real-world mobile robot-based pursuit evasion game. We validate our algorithms by experiments in a moderate-scale testbed in a challenging office environment. Overall, our work illustrates an innovative interplay between robotics and communication.
The work reported in this paper is motivated by the need for developing swarm pattern transformation methodologies.Two methods, namely a macroscopic method and a mathematical method are investigated for pattern transformation.The first method is based on macroscopic parameters while the second method is based on both microscopic and macroscopic parameters.A formal definition to pattern transformation considering four special cases of transformation is presented.Simulations on a physics sim ulation engine are used to confirm the feasibility of the proposed transformation methods.A brief comparison between the two methods is also presented.
Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigure static formations of non-holonomic vehicles endowed solely with local positioning capabilities. The convergence of our approach is mathematically proven and applied to a realistic robotic platform.
In this paper, we propose an acoustic-based head orientation estimation method using a microphone array mounted on a wheelchair, and apply it to a novel interface for controlling a powered wheelchair.The proposed interface does not require disabled people to wear any microphones or utter recognizable voice commands.By mounting the microphone array system on the wheelchair, our system can easily distinguish user utterances from other voices without using a speaker identification technique.The proposed interface is also robust to interference from surrounding noise.From the experimental results, we confirm the feasibility and effectiveness of the proposed method.
Mobile robotics environments must adopt networking solutions that provide secure and reliable communications for the mobile robots across wide areas such as hospitals, factories, farms, etc. This paper proposes a network architecture for large mobile robotic environments built above the existing networking infrastructures. The architecture builds an overlay network above the already deployed network. The overlay network must fulfill the requirements demanded by mobile robotic applications, mainly, communication continuity during handover, security, and quality of service. A prototype of this architecture was implemented and evaluated in a mobile robotic environment composed of Pioneer P3-DX mobile robots accessed through the Internet. Results from simulation show that the architecture scales well in larger networking scenarios.
In this paper, we study time-optimal trajectories for unmanned aerial vehicles (UAVs) to provide convoy protection to a group of stationary ground vehicles. The UAVs are modelled as Dubins vehicles flying at a constant altitude. Due to kinematic constraints of the UAVs, it is not possible for a single UAV to provide convoy protection indefinitely. In this paper, we derive time-optimal paths for a single UAV to provide continuous ground convoy protection for the longest possible time. Furthermore, this paper provides optimal trajectories for multiple UAVs to achieve uninterrupted convoy protection. The minimum number of UAVs required to achieve this task is determined.
A basic primitive in a networked robotic swarm is to form a connected component that covers some area with relatively uniform density. Although most approaches to the problem require local coordinate information, it has been proposed that robots with only connectivity information do this instead with a generalized form of diffusion-limited aggregation, in which robots wander randomly until they find a location where their topological constraints are satisfied and they are connected to a designated seed point. We find that the behavior of the algorithm varies qualitatively along a spectrum defined by the relative size of the total area, covered area, and initial distribution of robots. We identify and analyze five representative behaviors along this spectrum, finding that fast convergence can be expected only within a small range of parameters. Further, our results suggest that general coverage algorithms may require that the robotic swarm be coordinated across long distances.
Various coordination algorithms have been proposed for robot networks. One of the fundamental assumptions of such algorithms is that the underlying connectivity graph be stable. Adhoc routing protocols attempt to optimize the path from source to destination and do not guarantee route stability. We bridge this gap by providing directional and locational cues to the routing protocol to provide more stable routes. We implement our ideas on optimized link state routing (OLSR), a popular proactive routing protocol for robot networks. Our results show that simple directional and locational cues can achieve up to 20% fewer route switches in comparison to the basic version of OLSR.
Networked robots represent the convergence of robotics, sensor networks and mobile ad-hoc networks, with many applications and a growing market projected to be $200B in 2013. This talk will focus on some fundamental problems and practical issues underlying the deployment of large numbers of autonomously functioning robots. The central problem is the so-called inverse problem of deriving individual robot behaviors for a desired group behavior. There are numerous examples of group behavior in biology which suggest that analysis of swarming behaviors in biology may provide insight for the synthesis of collective behaviors for engineered systems. The author presents a methodology for modeling and analyzing such collective behaviors and discuss architectures, abstractions and algorithms for the control of large networks of robots.
This paper describes flexible software for industrial robots. WinRS232ROBOTcontrol and winEthernetROBOTcontrol software were developed to be used in industrial robots. With this software, industrial robots can be integrated in modern production systems, in an easy and efficient way. A Robotic Bar and a Flexible Manufacturing Cell (FMC) were developed with the objective of showing the potentialities of the developed software.
An efficient method for transmitting the robot control data over wireless Internet based on UDP protocol is proposed. The method allocates the highest priority to the robot control data and transmits them multiply from the base station. Simulation results show that very low packet delay and low packet errors can be achieved by the proposed method.
