A standing challenge in current intralogistics is to reliably, effectively yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this paper we explore a de-centralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and solutions to prevent or recover from deadlocks in specific situations are reported and discussed. We validate the approach empirically with simulated, large, heterogeneous multi-robot fleets (up to 100 robots tested) operating both in benchmark and realistic environments.
Cloud Computing is a versatile technology that can support a broad-spectrum of applications. The low cost of cloud computing and its dynamic scaling renders it an innovation driver for small companies, particularly in the developing world. Cloud deployed enterprise resource planning (ERP), supply chain management applications (SCM), customer relationship management (CRM) applications, medical applications, business applications and mobile applications have potential to reach millions of users. In this paper, we explore the different concepts involved in cloud computing and we also examine clouds from technical aspects. We highlight some of the opportunities in cloud computing underlining the importance of clouds showing why that technology must succeed and we have provided additional cloud computing problems that businesses may need to address. Finally, we discuss some of the issues that this area should deal with.
The synthesis of multi-fingered grasps on nontrivial objects requires a realistic representation of the contact between the fingers of a robotic hand and an object. In this work, we use a patch contact model to approximate the contact between a rigid object and a deformable anthropomorphic finger. This contact model is utilized in the computation of Independent Contact Regions (ICRs) that have been proposed as a way to compensate for shortcomings in the finger positioning accuracy of robotic grasping devices. We extend the ICR algorithm to account for the patch contact model and show the benefits of this solution.
The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.
Component-based software engineering is a common approach to develop and evolve contemporary software systems where different component sourcing options are available: 1)Software developed internally (in-house), 2)Software developed outsourced, 3)Commercial of the shelf software, and 4) Open Source Software.
However, there is little available research on what attributes of a component are the most important ones when selecting new components. The object of the present study is to investigate what matters the most to industry practitioners during component selection. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using Compositional Data Analysis. The descriptive results showed that Cost was clearly considered the most important attribute during the component selection. Other important attributes for the practitioners were: Support of the component, Longevity prediction, and Level of off-the-shelf fit to product. Next, an exploratory analysis was conducted based on the practitioners' inherent characteristics. Nonparametric tests and biplots were used. It seems that smaller organizations and more immature products focus on different attributes than bigger organizations and mature products which focus more on Cost.
Our work addresses issues related to the cohabitation of service robots and people in unstructured environments. We propose new planning techniques to empower robot means-end reasoning with the capability of taking into account human intentions and preferences. We also address the problem of human activity recognition in instrumented environments. We employ a constraint-based approach to realize a continuous inference process to attach a meaning to sensor traces as detected by sensors distributed in the environment.
A growing interest in the industrial sector for autonomous ground vehicles has prompted significant investment in fleet management systems. Such systems need to accommodate on-line externally imposed temporal and spatial requirements, and to adhere to them even in the presence of contingencies. Moreover, a fleet management system should ensure correctness, i.e., refuse to commit to requirements that cannot be satisfied. We present an approach to obtain sets of alternative execution patterns (called trajectory envelopes) which provide these guarantees. The approach relies on a constraint-based representation shared among multiple solvers, each of which progressively refines trajectory envelopes following a least commitment principle.
The main goal of this Chapter is to introduce SAM, an integrated architecture for concurrent activity recognition, planning and execution. SAM provides a general framework to define how an intelligent environment can assess contextual information from sensory data. The architecture builds upon a temporal reasoning framework operating in closed-loop between physical sensing and actuation components in a smart environments. The capabilities of the system as well as possible examples of its use are discussed in the context of the PEIS-Home, a smart environment integrated with robotic components.
Coordinating fleets of autonomous, non-holonomic vehicles is paramount to many industrial applications. While there exists solutions to efficiently calculate trajectories for individual vehicles, an effective methodology to coordinate their motions and to avoid deadlocks is still missing. Decoupled approaches, where motions are calculated independently for each vehicle and then centrally coordinated for execution, have the means to identify deadlocks, but not to solve all of them. We present a novel approach that overcomes this limitation and that can be used to complement the deficiencies of decoupled solutions with centralized coordination. Here, we formally define an extension of the framework of lattice-based motion planning to multi-robot systems and we validate it experimentally. Our approach can jointly plan for multiple vehicles and it generates kinematically feasible and deadlock-free motions.
There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations
A concept of a suspended robot for surface cleaning in silos is presented in this paper. The main requirements and limitations resulting from the specific operational conditions are discussed. Due to the large dimension of the silo as a confined space, specific kinematics of the robot manipulator is proposed. The major problems in its design are highlighted and an approach to resolve them is proposed. The suggested concept is a reasonable compromise between the basic contradicting factors in the design: small entrance and large surface of the confined space, suspension and stabilization of the robot
We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.
In recent years sensor networks have interested fields such as environment monitoring, surveillance and other distributed applications for data elaboration. This interest has been based on the decentralized approach in treating the information. However it is still a challenge to manipulate such streams of data when the dimension of the net becomes large despite computational capabilities and consumption constraints. In most of applications, location awareness is fundamental to accomplish common tasks. In this paper a probabilistic approach to solve localization problem in wireless sensor networks is presented. The algorithm, based on the Kalman Filter, estimates the sensors' location by an adaptive behavior. The technique proposed allows a reduction of the computation burden respect to the traditional Kalman Filter showing, as explained in simulations and real world experiments, good performances.
We propose an approach to configuration planning for robotic systems in which plans are represented as constraint networks and planning is defined as search in the space of such networks. The approach supports reasoning about time, resources, and information dependencies between actions. In addition, the system can leverage the flexibility of such networks at execution time to support dynamic goal posting and re-planning.
Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or “stigmergy”, to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.
This paper presents a localization algorithm for a small mobile platform. Taking advantage from modern technology, Saetta, a low cost mobile robot, has been built from scratch. Due to the limited processing capabilities, some ad hoc solutions have been used: the lack of processing resources has been compensated by an efficient implementation of the estimator and by the use of compass measures which ease the computational load. The results show how a careful design allows the implementation of sophisticated algorithms also on small platforms.
This article addresses the fast solution of a Quadratic Program underlying a Linear Model Predictive Control scheme that generates walking motions. We introduce an algorithm which is tailored to the particular requirements of this problem, and therefore able to solve it efficiently. Different aspects of the algorithm are examined, its computational complexity is presented, and a numerical comparison with an existing state of the art solver is made. The approach presented here, extends to other general problems in a straightforward way. © 2009 IEEE.
The article presents an improved formulation of an existing model predictive control scheme used to generate online "stable" walking motions for a humanoid robot. We introduce: (i) a change of variable that simplifies the optimiza tion problem to be solved; (ii) a simply bounded formulation in the case when the positions of the feet are predetermined; (iii) a formulation allowing foot repositioning (when the system is perturbed) based on ℓ1- and ℓ∞-norm minimization; (iv) a formulation that accounts for (approximate) double support constraints when foot repositioning occurs.
This article presents a comparison between dense and sparse model predictive control (MPC) formulations, in the context of walking motion generation for humanoid robots. The former formulation leads to smaller, the latter one to larger but more structured optimization problem. We put an accent on the sparse formulation and point out a number of advantages that it presents. In particular, motion generation with variable center of mass (CoM) height, as well as variable discretization of the preview window, come at a negligible additional computational cost. We present a sparse formulation that comprises a diagonal Hessian matrix and has only simple bounds (while still retaining the possibility to generate motions for an omnidirectional walk). Finally, we present the results from a customized code used to solve the underlying quadratic program (QP).
We investigate the changes under small perturbations of the canonical structure information for a system pencil (A B C D) − s (E 0 0 0), det(E) ≠ 0, associated with a (generalized) linear time-invariant state-space system. The equivalence class of the pencil is taken with respect to feedback-injection equivalence transformation. The results allow to track possible changes under small perturbations of important linear system characteristics.
In the present paper we describe the design of a fuzzy gain scheduler for tracking a reference trajectory of a nonlinear autonomous system. The proposed fuzzy gain scheduling method has two major advantages over the existing crisp gain scheduling methods. First, it provides a general and formally motivated method for the interpolation of available local control laws into a global gain scheduling control law. Second, the method for determining the weights of the local control laws in the global gain scheduling control law is general and computationally efficient. It is shown that a fuzzy gain scheduler can be designed such that robust asymptotic stability is met. Finally, an LQR control design based method is presented
We consider the problem of calculating forces on high current solid conductors, as is present in various types of electrical installations e.g. in substations [1]. An example of such an installation with three parallel conductors is shown in Figure 1. The conductor forces are important for the design of the station, in particular for the conductor geometry and mechanical support.
The report describes an investigative assignment done for Lantmännen Unibake, Korvbrödsbagarn, where they wanted an automated solution for packaging bags into boxes to replace the manual handling that they have today.
The method used to find a suitable solution was the PDCA-cycle (Plan-Do-Check-Act), where four different phases are reviewed to get an understanding of the present and the desired solution, investigate available alternatives trough contact with agents for machinery companies, evaluation of the solutions and for last a conclusion and suggestion for continuing the work.
Trends like Internet of Things (IoT), 5G and Cloud are pushing for device connectivity to the Internet, which affects industrial embedded systems with e.g., an increase in code base and functionality. Due to different application requirements, there are relative little reuse between embedded systems with different run-time context (from super loop to multi-threaded), and different types of communication (best effort and real time). In order to improve code reuse and changeability, we propose a flexible communication stack design, that can be configured for time sensitive communication with a real-time operating system (RTOS), or configured for best effort communication with either a super loop or an operating system (OS). Experiments demonstrate the flexibility and simplicity of the design with different configurations, e.g., super loop, single threaded, multi-threaded. Measurements show that the variations in performance related to run-time context scales as expected.
While energy consumption is widely considered the primary challenge of wireless networked devices, energy harvesting emerges as a promising way of powering the Internet of Things (IoT). In the Medium Access Control (MAC) layer of the communication stack, energy harvesting introduces spatial and temporal uncertainty in the availability of energy. In this context, this paper focuses on the design and implementation of the MAC layer of wireless embedded systems that are powered by energy harvesting; providing novel protocol features and practical experiences to designers of consumer electronics who opt for tailoring their own protocol solutions instead of using the standards.
One of the fundamental building blocks of a Wireless Sensor Network (WSN) is the Medium Access Control (MAC) protocol, that part of the system governing when and how two independent neighboring nodes activate their respective transceivers to directly interact. Historically, data exchange has always been initiated by the node willing to relay data, i.e. the sender. However, the Receiver-Initiated paradigm introduced by Lin et al. in 2004 with RICER and made popular by Sun et al. in 2008 with RI-MAC, has spawned a whole new stream of research, yielding tens of new MAC protocols. Within such paradigm, the receiver is the one in charge of starting a direct communication with an eligible sender. This allows for new useful properties to be satisfied, novel schemes to be introduced and new challenges to be tackled. In this paper, we present a survey comprising of all the MAC protocols released since the year 2004 that fall under the receiver-initiated category. In particular, keeping in mind the key challenges that receiver-initiated MAC protocols are meant to deal with, we analyze and discuss the different protocols according to common features and design goals. The aim of this paper is to provide a comprehensive and self-contained introduction to the fundamentals of the receiver-initiated paradigm, providing newcomers with a quick-start guide on the state of the art of this field and a palette of options, essential for implementing applications or designing new protocols.
In receiver-initiated medium access control (MAC) protocols for wireless sensor networks, communication is initiated by the receiver node which transmits beacons indicating its availability to receive data. In the case of multiple senders having traffic for a given receiver, such beacons form points where collisions are likely to happen. In this paper, we present altruistic backoff (AB), a novel collision avoidance mechanism that aims to avoid collisions before the transmission of a beacon. As a result of an early backoff, senders spend less time in idle listening waiting for a beacon, thus saving significant amounts of energy. We present an implementation of AB for Texas Instruments’ eZ430-rf2500 sensor nodes and we evaluate its performance with simulations and experiments.
Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.
Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.
In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.
Inverters are a bridge for DC resources that want to supply AC loads. They can be connected to different resources such as batteries or solar panels on their DC side, and through their AC terminal, get connected to different types of AC loads and supply them. Due to the high flexibility in the layout design of multilevel inverters, these inverters are used widely in various applications. One of the most common applications of multi-level inverters is in microgrids, where DC resources with different characteristics and most probably, unstable voltage exists. In general voltage output of inverters, are problematic and might be not close to an ideal sinusoid. Using different types of resources for connecting them to the DC side of inverters, causes different harmonics that their amplitude and THD changes in the different situation. Considering real-world DC resources, where their voltage might change suddenly or overtime, the problem of controlling harmonics and THD dynamically is more significant. In this paper for overcoming these harmonics under instability, a new comprehensive algorithm has been introduced by using a closed-loop PI controller in multi-carrier power width modulation switching that can control the selected harmonics, or lower the produced THD, in both steady DC voltage and unstable situations such as voltage unbalancing of DC resources.
Generators are at the heart of power systems. Accurate field tests are necessary to ensure the reliable and robust functioning of generators. As the systems become complex, real world tests demand high costs and technical complexity. Due to these restrictions, simulators are used as an alternative. This paper presents a novel simulator, which is benefiting from using a machine model in a stand-alone device as a simulator for simulating a power generator. The simulator has been implemented on a stand-alone board using DSP and FPGA. The main purpose of this simulator is to test and analysis new devices with a power generator and their behaviors. Physical specifications and equations which are used in MAPNA RealTime Power System Simulator (MRTPS) are described. The simulator is connected to a real exciter panel and several tests carried out. The experimental results show that the simulator can be used as an alternative to real generator and simulation results provide a high degree of accuracy.
This paper investigates the data aggregation problem for a multi-agent system. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, agents are assumed to dynamically gather data over time, i.e., a dynamic scenario. A protocol for distributed data aggregation which is proved to converge to the basic belief assignment (BBA) given by a centralized aggregation based on the Transferable Belief Model (TBM) is provided.
In this paper the data aggregation problem for a multi-agent system is investigated. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, each agent is supposed to provide an observation which does not change over time, i.e., static scenario. A protocol for distributed data aggregation which is proved to converge to the basic belief assignment (BBA) given by a centralized aggregation based on the Transferable Belief Model (TBM) is provided.
This paper presents an approach to complex system recovery based on a fuzzy specification method. The method can be applied when controllers of different types and makes are connected in a common control architecture. The method allows a controller B to trigger a recovery operation on a controller A when B has the recovery specification of A. The approach has important potential applications in industry, e.g. as a possible complement to PLC standards such as IEC1131, and to the design of hybrid and complex control systems
Explainable AI has recently paved the way to justify decisions made by black-box models in various areas. However, a mature body of work in the field of affect detection is still limited. In this work, we evaluate a black-box outcome explanation for understanding humans’ affective states. We employ two concepts of Contextual Importance (CI) and Contextual Utility (CU), emphasizing on a context-aware decision explanation of a non-linear model, mainly a neural network. The neural model is designed to detect the individual mental states measured by wearable sensors to monitor the human user’s well-being. We conduct our experiments and outcome explanation on WESAD and MAHNOB-HCI, as multimodal affect computing datasets. The results reveal that in the first experiment the electrodermal activity, respiration as well as accelorometer and in the second experiment the electrocardiogram and respiration signals contribute significantly in the classification task of mental states for a specific participant. To the best of our knowledge, this is the first study leveraging the CI and CU concepts in outcome explanation of an affect detection model.
This paper presents a control scheme for localizing and encircling a target using a multi-robot system. The task is achieved in a distributed way, in that each robot only uses local information gathered by on-board relative-position sensors assumed to be noisy, anisotropic, and unable to detect the identity of the measured object. Communication between the robots is provided by limited-range transceivers. Experimental results with stationary and moving targets support the theoretical analysis.
This paper addresses the problem of simultaneous localisation and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling non-linearities compared to other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented
In this paper we present a study on the effects of auditory- and haptic feedback in a virtual throwing task performed with a point-based haptic device. The main research objective was to investigate if and how task performance and perceived intuitiveness is affected when interactive sonification and/or haptic feedback is used to provide real-time feedback about a movement performed in a 3D virtual environment. Emphasis was put on task solving efficiency and subjective accounts of participants’ experiences of the multimodal interaction in different conditions. The experiment used a within-subjects design in which the participants solved the same task in different conditions: visual-only, visuohaptic, audiovisual and audiovisuohaptic. Two different sound models were implemented and compared. Significantly lower error rates were obtained in the audiovisuohaptic condition involving movement sonification based on a physical model of friction, compared to the visual-only condition. Moreover, a significant increase in perceived intuitiveness was observed for most conditions involving haptic and/or auditory feedback, compared to the visual-only condition. The main finding of this study is that multimodal feedback can not only improve perceived intuitiveness of an interface but that certain combinations of haptic feedback and movement sonification can also contribute with performance-enhancing properties. This highlights the importance of carefully designing feedback combinations for interactive applications.
A growing body of literature shows that endowing a mobile robot with semantic knowledge and with the ability to reason from this knowledge can greatly increase its capabilities. In this paper, we present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, and standard perception and navigation routines. (C) 2013 Elsevier B.V. All rights reserved.
This paper deals with the use of semantic knowledge to improve the intelligence and autonomous behavior of a mobile robot. A robot can exploit the semantics of its environment to infer new, implicit information. Another interesting possibility is to use semantics for detecting deviations between the real world and what is supposed to be ``normal''. For instance, normative semantic knowledge may state that towels should stay in the bathroom. If a robot detects a towel in the kitchen, it can react and decide to solve this inconsistency by bringing it to the bathroom. However not all ways to solve an inconsistency are acceptable: for instance, if the robot put the towel temporarily on a dirty sink in order to re-grasp it with the other arm, it would violate another norm -- namely, that towels should always stay on a clean surface. In this work we present an algorithm that detects and recovers from norm violations, according to a semantic representation of norms, and ensures the normative acceptability of the robot actions throughout execution.