Author name cluster

Pedro U. Lima

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

39 papers

2 author rows

IROS Conference 2025 Conference Paper

Frontier Shepherding: A Bio-inspired Multi-robot Framework for Large-Scale Exploration

John Lewis
Meysam Basiri
Pedro U. Lima

Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes Frontier Shepherding (FroShe), a bio-inspired multi-robot framework for large-scale exploration. The framework heuristically models frontier exploration based on the shepherding behavior of herding dogs, where frontiers are treated as a swarm of sheep reacting to robots modeled as shepherding dogs. FroShe is robust across varying environment sizes and obstacle densities, requiring minimal parameter tuning for deployment across multiple agents. Simulation results demonstrate that the proposed method performs consistently, regardless of environment complexity, and outperforms state-of-the-art exploration strategies by an average of 20% with three UAVs. The approach was further validated in real-world experiments using single-and dual-drone deployments in a forest-like environment.

IROS Conference 2025 Conference Paper

Large Language Model-Based Robot Task Planning from Voice Command Transcriptions

Afonso Certo
Bruno Martins
Carlos Azevedo
Pedro U. Lima

One of the primary challenges in building a General Purpose Service Robot (GPSR), i. e. a robot capable of executing generic human commands, lies in acting upon natural language instructions. These instructions often contain speech recognition errors and incomplete information, complicating the extraction of clear goals and the formulation of an efficient and effective action plan. This work presents a pipeline that leverages a Large Language Model to directly translate instruction transcripts into coherent action plans. The pipeline also integrates environmental context into the model’s input, allowing for the generation of more efficient and context-aware plans. The system’s performance was evaluated using a simulator based on generalized stochastic Petri Nets, achieving a success rate of around 55% on the ALFRED dataset, even in unseen environments. The entire pipeline was also successfully deployed at RoboCup 2024 in Eindhoven, where it secured second place in the GPSR task. The code, dataset, and models are available at https://github.com/socrob/llm_gpsr.

ICRA Conference 2024 Conference Paper

Learning-based Model Predictive Control for an Autonomous Formula Student Racing Car

David R. Gomes
Miguel Ayala Botto
Pedro U. Lima

Advancements in Automated Driving Systems (ADSs) have enabled the achievement of a certain level of autonomy while commuting in a car. However, emergency and high-speed maneuvers still arise as significant challenges for ADSs due to the intrinsic nonlinearity and fast-paced behavior of such events. These maneuvers are a distinctive feature within the recently established motorsport discipline of Autonomous Racing (AR). In this work, we explore the use of Learning-based Model Predictive Control (LMPC) to address possible model mismatches of the first principles model in high-speed racing. To this end, a Model Predictive Contouring Control (MPCC) (a specific formulation of the standard Model Predictive Control, MPC) is formulated, and a Neural Network (NN) that leverages the use of Feedforward and Recurrent layers is employed to learn the errors of the first principles model. By combining the NN with the first principles model, the LMPC is born, capable of accurately predicting the future with a computational effort compatible with real-time feasibility, effectively handling the vehicle at its limits. Furthermore, the controller can adapt to changing environments by training the NN during the race. The MPCC (formulation without the NN) is deployed on a real autonomous formula student racing car showing an improvement of 16 % in mean lap times across the same track between a common geometric controller. The LMPC is analyzed in a high-fidelity simulator, achieving an improvement of 8. 9 % in mean lap times when compared to the MPCC.

ICRA Conference 2022 Conference Paper

An observer cascade for velocity and multiple line estimation

André Mateus 0001
Pedro U. Lima
Pedro Miraldo

Previous incremental estimation methods consider estimating a single line, requiring as many observers as the number of lines to be mapped. This leads to the need for having at least 4N state variables, with N being the number of lines. This paper presents the first approach for multi-line incremental estimation. Since lines are common in structured environments, we aim to exploit that structure to reduce the state space. The modeling of structured environments proposed in this paper reduces the state space to 3N + 3 and is also less susceptible to singular configurations. An assumption the previous methods make is that the camera velocity is available at all times. However, the velocity is usually retrieved from odometry, which is noisy. With this in mind, we propose coupling the camera with an Inertial Measurement Unit (IMU) and an observer cascade. A first observer retrieves the scale of the linear velocity and a second observer for the lines mapping. The stability of the entire system is analyzed. The cascade is shown to be asymptotically stable and shown to converge in experiments with simulated data.

IROS Conference 2020 Conference Paper

Long-Run Multi-Robot Planning under Uncertain Action Durations for Persistent Tasks

Carlos Azevedo
Bruno Lacerda
Nick Hawes
Pedro U. Lima

This paper presents an approach for multi-robot long-term planning under uncertainty over the duration of actions. The proposed methodology takes advantage of generalized stochastic Petri nets with rewards (GSPNR) to model multi-robot problems. A GSPNR allows for unified modeling of action selection, uncertainty on the duration of action execution, and for goal specification through the use of transition rewards and rewards per time unit. Our approach relies on the interpretation of the GSPNR model as an equivalent embedded Markov reward automaton (MRA). We then build on a state-of-the-art method to compute the long-run average reward over MRAs, extending it to enable the extraction of the optimal policy. We provide an empirical evaluation of the proposed approach on a simulated multi-robot monitoring problem, evaluating its performance and scalability. The results show that the synthesized policy outperforms a policy obtained from an infinite horizon discounted reward formulation as well as a carefully hand-crafted policy.

ICAPS Conference 2019 Conference Paper

A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs

Tiago S. Veiga
Miguel Silva
Rodrigo M. M. Ventura
Pedro U. Lima

Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e. g. , a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.

IROS Conference 2018 Conference Paper

A Probabilistic Approach to Benchmarking and Performance Evaluation of Robot Systems

Pedro U. Lima

Problem benchmarks are used in experimental science as a reference against which results of experiments using distinct approaches to solve the problem are compared and evaluated in relative terms. In Robotics, just formulating a general performance assessment problem is difficult per se, as robot systems are composed of very diverse subsystems (e. g. , localisation, human-robot interaction, task planning, motion planning). This paper introduces a probabilistic approach to benchmarking and evaluating performance of robot systems, which uses probability theory as the common language to quantify the performance of distinct functionalities of a robot system and their impact on the performance of a task carried out by that system. The approach can be used to analyse the performance of a task plan from the performances if its composing functionalities, or to (re)plan when a performance degradation in functionality is predicted to cause performance degradation of the task plan beyond acceptable limits.

AAMAS Conference 2018 Conference Paper

Towards Institutions for Mixed Human-Robot Societies

Stevan Tomic
Alicja Wasik
Pedro U. Lima
Alcherio Martinoli
Federico Pecora
Alessandro Saffiotti

We report an exploration into normative reasoning for robots in human societies using the concept of institutions.

IROS Conference 2018 Conference Paper

Towards Norm Realization in Institutions Mediating Human-Robot Societies

Alicja Wasik
Stevan Tomic
Alessandro Saffiotti
Federico Pecora
Alcherio Martinoli
Pedro U. Lima

Social norms are the understandings that govern the behavior of members of a society. As such, they regulate communication, cooperation and other social interactions. Robots capable of reasoning about social norms are more likely to be recognized as an extension of our human society. However, norms stated in a form of the human language are inherently vague and abstract. This allows for applying norms in a variety of situations, but if the robots are to adhere to social norms, they must be capable of translating abstract norms to the robotic language. In this paper we use a notion of institution to realize social norms in real robotic systems. We illustrate our approach in a case study, where we translate abstract norms into concrete constraints on cooperative behaviors of humans and robots. We investigate the feasibility of our approach and quantitatively evaluate the performance of our framework in 30 real experiments with user-based evaluation with 40 participants.

IROS Conference 2016 Conference Paper

Efficient object search for mobile robots in dynamic environments: Semantic map as an input for the decision maker

Tiago S. Veiga
Pedro Miraldo
Rodrigo M. M. Ventura
Pedro U. Lima

In this work we study the efficient search of objects in domestic environments, using probabilistic logic to represent uncertainty about object location and partially observable Markov decision processes (POMDP) for the decision-making process regarding the movements to be carried out by the robot to improve its belief about the object locations. We propose the use of a semantic map that stores information about the knowledge in the system and updates it, by an inference process, with sensor information received from the object recognition module. However, semantic maps are not capable of actively search for more information in the environment. For that reason a decision-making module, based on a POMDP framework, is integrated in the system. Several experiments were made in a realistic apartment test bed using every day objects and a mobile robot, showing that this hybrid solution makes the search process more efficient.

IROS Conference 2016 Conference Paper

Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation

Alicja Wasik
José N. Pereira
Rodrigo M. M. Ventura
Pedro U. Lima
Alcherio Martinoli

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real-world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity.

ICRA Conference 2016 Conference Paper

On-board vision-based 3D relative localization system for multiple quadrotors

Duarte Dias
Rodrigo M. M. Ventura
Pedro U. Lima
Alcherio Martinoli

This work proposes a novel relative localization system, based on active markers and an on-board camera, for tracking multiple quadrotors in a limited field of view. The system extracts the 3D poses of the markers including one that, by pulsating at a predefined frequency, provides an unique platform ID. We discuss how the camera field of view can be explored in presence of multiple targets, and what are the conditions on the system visibility that lead to the establishment of bidirectional sensing between robots with similar sensing capabilities. A visibility analysis is conducted to show that the developed relative localization system meets such requirements, and a closed-loop experiment is used to validate its performance under these conditions. Finally, its performance is compared with other results from the literature, and a metric is established with the intent of mapping different design solutions, facilitating design choices in presence of different requirements.

IROS Conference 2015 Conference Paper

Augmented reality on robot navigation using non-central catadioptric cameras

Tiago J. Dias
Pedro Miraldo
Nuno Gonçalves 0001
Pedro U. Lima

In this paper we present a framework for the application of augmented reality to a mobile robot, using non-central camera systems. Considering a virtual object in the world with known local 3D coordinates, the goal is to project this object into the image of a non-central catadioptric imaging device. We propose a solution to this problem which allows us to project textured objects to the image in real-time (up to 20 fps): projection of 3D segments to the image; occlusions; and illumination. In addition, since we are considering that the imaging device is on a mobile robot, one needs to take into account the real-time localization of the robot. To the best of our knowledge this is the first time that this problem is addressed (all state-of-the-art methods are derived for central camera systems). To evaluate the proposed framework we test the solution using a mobile robot and a non-central catadioptric camera (using a spherical mirror).

ICRA Conference 2015 Conference Paper

Decentralized target tracking based on multi-robot cooperative triangulation

André Dias
Jesús Capitán
Luis Merino
José Almeida 0001
Pedro U. Lima
Eduardo P. da Silva

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.

ICRA Conference 2014 Conference Paper

Audio-based localization for swarms of micro air vehicles

Meysam Basiri
Felix Schill
Dario Floreano
Pedro U. Lima

Localization is one of the key challenges that needs to be considered beforehand to design truly autonomous MAV teams. In this paper, we present a cooperative method to address the localization problem for a team of MAVs, where individuals obtain their position through perceiving a sound-emitting beacon MAV that is flying relative to a reference point in the environment. For this purpose, an on-board audio-based localization system is proposed that allows individuals to measure the relative bearing to the beacon robot and furthermore to localize themselves and the beacon robot simultaneously, without the need for a communication network. Our method is based on coherence testing among signals of a small on-board microphone array, to obtain the relative bearing measurements, and an estimator, to fuse these measurements with sensory information about the motion of the robot throughout time, to estimate robustly the MAV positions. The proposed method is evaluated both in simulation and in real world experiments.

JAAMAS Journal 2014 Journal Article

Decision-theoretic planning under uncertainty with information rewards for active cooperative perception

Matthijs T. J. Spaan
Tiago S. Veiga
Pedro U. Lima

Abstract Partially observable Markov decision processes (POMDPs) provide a principled framework for modeling an agent’s decision-making problem when the agent needs to consider noisy state estimates. POMDP policies take into account an action’s influence on the environment as well as the potential information gain. This is a crucial feature for robotic agents which generally have to consider the effect of actions on sensing. However, building POMDP models which reward information gain directly is not straightforward, but is important in domains such as robot-assisted surveillance in which the value of information is hard to quantify. Common techniques for uncertainty reduction such as expected entropy minimization lead to non-standard POMDPs that are hard to solve. We present the POMDP with Information Rewards (POMDP-IR) modeling framework, which rewards an agent for reaching a certain level of belief regarding a state feature. By remaining in the standard POMDP setting we can exploit many known results as well as successful approximate algorithms. We demonstrate our ideas in a toy problem as well as in real robot-assisted surveillance, showcasing their use for active cooperative perception scenarios. Finally, our experiments show that the POMDP-IR framework compares favorably with a related approach on benchmark domains.

IROS Conference 2013 Conference Paper

An experimental study in wireless connectivity maintenance using up to 40 robots coordinated by an institutional robotics approach

José N. Pereira
Porfírio Silva
Pedro U. Lima
Alcherio Martinoli

This work is developed in the framework of Institutional Robotics (IR), an approach to cooperative distributed robotic systems that draws inspiration from the social sciences. We consider a case study concerned with a swarm of simple robots which has to maintain wireless connectivity and a certain degree of spatial compactness. Robots have local, bounded communication capabilities and have to execute the task (running an IR controller) using exclusively as information their current number of wireless connections to neighbors. For the very same case study, we previously introduced an IR-based macroscopic model for the behavior of a large number of robots, validated using a submicroscopic model implemented through a realistic simulator. In this work, we go a step further and validate our submicroscopic model with real world experiments, duplicating accurately the conditions used, including a large number of robots and noisy communication channels. The main conclusions of this paper are two-fold. First, the IR approach was able to maintain the wireless connectivity of a swarm of 40 real, resource-constrained robots. This speaks in favor of the robustness and scalability of such approach. Second, the submicroscopic model implemented is faithfully capturing the reality and can be used to further optimize the performances of distributed control strategies using an IR approach.

ICRA Conference 2013 Conference Paper

Cooperative robot localization and target tracking based on least squares minimization

Aamir Ahmad
Gian Diego Tipaldi
Pedro U. Lima
Wolfram Burgard

In this paper we address the problem of cooperative localization and target tracking with a team of moving robots. We model the problem as a least squares minimization problem and show that this problem can be efficiently solved using sparse optimization methods. To achieve this, we represent the problem as a graph, where the nodes are robot and target poses at individual time-steps and the edges are their relative measurements. Static landmarks at known position are used to define a common reference frame for the robots and the targets. In this way, we mitigate the risk of using measurements and state estimates more than once, since all the relative measurements are i. i. d. and no marginalization is performed. Experiments performed using a set of real robots show higher accuracy compared to a Kalman filter.

AAMAS Conference 2013 Conference Paper

Multiagent POMDPs with Asynchronous Execution

João V. Messias
Matthijs T. J. Spaan
Pedro U. Lima

The Multiagent POMDP (MPOMDP) framework provides well-known methods to model and solve fully communicative multiagent problems. However, the size of these models grows exponentially in the number of agents, and agents are required to act in synchrony. We show how these problems can be mitigated through an event-driven, asynchronous formulation of the MPOMDP dynamics. We can prove that the optimal value function in our framework is piecewise linear and convex, allowing us to extend a standard point-based solver to the event-driven setting. We also show how belief states can be updated at run-time in asynchronous domains. Our results show that asynchronous models scale better to larger domains than synchronous analogues, while retaining solution quality.

ICRA Conference 2013 Conference Paper

Perception-driven multi-robot formation control

Aamir Ahmad
Tiago Pereira do Nascimento
André Gustavo Scolari Conceição
António Paulo Moreira
Pedro U. Lima

Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

IROS Conference 2012 Conference Paper

Robust acoustic source localization of emergency signals from Micro Air Vehicles

Meysam Basiri
Felix Schill
Pedro U. Lima
Dario Floreano

In search and rescue missions, Micro Air Vehicles (MAV's) can assist rescuers to faster locate victims inside a large search area and to coordinate their efforts. Acoustic signals play an important role in outdoor rescue operations. Emergency whistles, as found on most aircraft life vests, are commonly carried by people engaging in outdoor activities, and are also used by rescue teams, as they allow to signal reliably over long distances and far beyond visibility. For a MAV involved in such missions, the ability to locate the source of a distress sound signal, such as an emergency whistle blown by a person in need of help, is therefore significantly important and would allow the localization of victims and rescuers during night time, through foliage and in adverse conditions such as dust, fog and smoke. In this paper we present a sound source localization system for a MAV to locate narrowband sound sources on the ground, such as the sound of a whistle or personal alarm siren. We propose a method based on a particle filter to combine information from the cross correlation between signals of four spatially separated microphones mounted on the MAV, the dynamics of the aerial platform, and the doppler shift in frequency of the sound due to the motion of the MAV. Furthermore, we evaluate our proposed method in a real world experiment where a flying micro air vehicle is used to locate and track the position of a narrowband sound source on the ground.

AAMAS Conference 2011 Conference Paper

Designing Petri Net Supervisors for Multi-Agent Systems from LTL Specifications

Bruno Lacerda
Pedro U. Lima

In this paper, we use LTL to specify acceptable/desirable behaviours for a system modelled as a Petri net, and create a Petri net realization of a supervisor that is guaranteed to enforce them, by appropriately restricting the uncontrolled behaviour of the system. We illustrate the method with an application to the specification of coordination requirements between the members of a team of simulated soccer robots.

IROS Conference 2011 Conference Paper

LTL-based decentralized supervisory control of multi-robot tasks modelled as Petri nets

Bruno Lacerda
Pedro U. Lima

We present a decentralized methodology to control multi-robot systems, where each robot behaviour is modelled as a Petri net (PN) and a set of coordination rules between the robots is given as linear temporal logic (LTL) formulas describing safety properties for the system. The LTL formulas are used to define the events and changes in state that must be communicated between robots and to augment the individual PN model of each robot so that it can handle the incoming communications. These augmented PNs are then used, in conjunction with the LTL formulas, to build PN realizations of local supervisors, based on discrete event system theory, that enforce the LTL specifications by construction. The methodology is illustrated through a simulated application example.

IROS Conference 2010 Conference Paper

Active cooperative perception in network robot systems using POMDPs

Matthijs T. J. Spaan
Tiago S. Veiga
Pedro U. Lima

Network robot systems (NRS) provide many scientific and technological challenges, given that robots interact with each other as well as with sensors present in the environment to accomplish certain tasks. In this work, we consider an essential problem in NRS, namely how to perform task planning given the limitations both in on-board sensing as well as in the environment's sensors. Partially observable Markov decisions processes (POMDPs) form an attractive framework to address planning in the uncertain environments that typify NRS. We show how to model a typical cooperative perception task in a NRS, namely tracking and classifying people, and we present experiments that show how the proposed approach results in an effective interplay between robot and environment sensors.

IROS Conference 2010 Conference Paper

Fault-tolerant probabilistic sensor fusion for Multi-Agent Systems

Abdolkarim Pahliani
Matthijs T. J. Spaan
Pedro U. Lima

In this work we focus on the problem of probabilistic sensor fusion in Multi-Robot Multi-Sensor Systems (MRMS), taking into account that some sensors might fail or produce erroneous information. We study fusion methods that can successfully cope with situations of agreement, partial agreement, and disagreement between sensors. We define a set of specifications for fusion methods appropriate for MRMS environments. In light of these specifications, we review two popular algorithms for probabilistic sensor fusion, Linear Opinion Pool (LOP) and Logarithmic Opinion Pool (LGP). To overcome difficulties of applying them to a MRMS setting, a new method is introduced, p-norm Opinion Pool (POP). Comparing to LOP and LGP, POP is more compatible with the specifications and more flexible, successfully handling situations of agreement and disagreement between sensors. Through simulation and real-world experiments, we check performance of the POP and compare it with LOP and LGP. We also implement a real-world experiment through which the performance of POP is examined.

ICAPS Conference 2009 Conference Paper

A Decision-Theoretic Approach to Dynamic Sensor Selection in Camera Networks

Matthijs T. J. Spaan
Pedro U. Lima

Nowadays many urban areas have been equipped with networks of surveillance cameras, which can be used for automatic localization and tracking of people. However, given the large resource demands of imaging sensors in terms of bandwidth and computing power, processing the image streams of all cameras simultaneously might not be feasible. In this paper, we consider the problem of dynamical sensor selection based on user-defined objectives, such as maximizing coverage or improved localization uncertainty. We propose a decision-theoretic approach modeled as a POMDP, which selects k sensors to consider in the next time frame, incorporating all observations made in the past. We show how, by changing the POMDP's reward function, we can change the system's behavior in a straightforward manner, fulfilling the user's chosen objective. We successfully apply our techniques to a network of 10 cameras.

IROS Conference 2009 Conference Paper

Decision-theoretic robot guidance for active cooperative perception

Abdolkarim Pahliani
Matthijs T. J. Spaan
Pedro U. Lima

We consider the problem of sensor-aware path planning for a robot in a networked robot system, in particular in urban environments equipped with a network of surveillance cameras. A robot can use observations from the camera network to improve its own localization performance, but also needs to take into account the specifics of its local sensors. We model our problem in the Markov decision process framework, which forms a natural way to express concurrent and possibly conflicting objectives - such as reaching a goal quickly, keeping the robot localized, keeping the target in sight - each with their own priority. We show how we can successfully prioritize the different objectives in a flexible way by changing the reward function, based on the sensory needs of the system.

IROS Conference 2009 Conference Paper

ISROBOTNET: A testbed for sensor and robot network systems

Marco Barbosa
Alexandre Bernardino
Dario Figueira
José António Gaspar
Nelson Gonçalves
Pedro U. Lima
Plinio Moreno
Abdolkarim Pahliani

This paper introduces a testbed for sensor and robot network systems, currently composed of 10 cameras and 5 mobile wheeled robots equipped with several sensors for self-localization, obstacle avoidance and vision cameras, and wireless communications. The testbed includes a service-oriented middleware to enable fast prototyping and implementation of algorithms previously tested in simulation, as well as to simplify integration of subsystems developed by different partners. We survey an integrated approach to human-robot interaction that has been developed supported by the testbed under an European research project. The application integrates innovative methods and algorithms for people tracking and waving detection, cooperative perception among static and mobile cameras to improve people tracking accuracy, as well as decision-theoretical approaches to sensor selection and task allocation within the sensor network.

IROS Conference 2007 Conference Paper

Cooperative opinion pool: a new method for sensor fusion by a robot team

Abdolkarim Pahliani
Pedro U. Lima

In this work we overview two popular algorithms for sensor fusion, linear opinion pool (LOP) and logarithmic opinion pool (LGP) and introduce a new method to overcome their difficulties: cooperative opinion pool (COP). COP considers all of the dependencies between observations such as LOP and reduces the uncertainty such as LGP. We check its performance on a simulated multi-robot environment, where a group of robots cooperate to reduce uncertainty of self- localization and object localization. Simulation results show that the entropy of cooperative localization is reduced as the number of cooperating robots grows.

IROS Conference 2007 Conference Paper

Eliciting preferences over observed behaviours based on relative evaluations

Valdinei Freire da Silva
Pedro U. Lima
Anna Helena Reali Costa

Reinforcement learning addresses the question of programming an autonomous agent to execute tasks that are described as reinforcement functions. Then, the agent is responsible for discovering the best actions to fulfil such task. Most of the work on reinforcement learning considers that reinforcements are given by the environment, not addressing the problem of how to describe tasks as reinforcement functions. Preference elicitation addresses the problem of describing a human preference through utility functions, from which reinforcement functions are special cases. This paper proposes an approach where preference elicitation and reinforcement learning are handled in an integrated manner, providing an autonomous method of programming an agent. The agent is programmed through pairwise evaluations over observed behaviours of the agent, where the evaluations are summarised in the reinforcement function. In this paper we present an approach to solve such a problem based on evaluations over observed behaviours. We propose a new algorithm, PEOB-RS, that can be shown to converge towards an optimal policy, providing the number of trials for each behaviour tends to infinity. Experimental results from learning in a grid stochastic environment are used to obtain a reinforcement function, illustrating the effectiveness of PEOB-RS, even if requiring too many evaluations. Such reinforcement function is then transferred to a more real-like environment simulating a pioneer robot, showing the abstraction property of utility functions.

IROS Conference 2007 Conference Paper

Modelling, analysis and execution of robotic tasks using petri nets

Hugo Costelha
Pedro U. Lima

This paper introduces Petri net based models of robotic tasks, which can be used to analyse and synthesise task plans, taking into account a Petri net model that abstracts the relevant features from the robot environment as well. Logical analysis concerning deadlocks and resource conservation can be performed over the ordinary version of the model. A task plan modeled by a Petri net can be extracted from the generalised stochastic version of the model, representing the optimal plan given a probabilistic measure of uncertainty associated to the effects of its composing actions. The Petri net representing the model is suitable for being ran directly within the code, as well as for plan monitoring during execution time. Simulation results illustrating the methodology are presented for a robotic soccer scenario.

IROS Conference 2007 Conference Paper

On the use of perspective catadioptric sensors for 3D model-based tracking with particle filters

Matteo Tajana
José António Gaspar
Jacinto C. Nascimento
Alexandre Bernardino
Pedro U. Lima

We present a model-based 3D tracking system, using wide angle perspective catadioptric sensors. These sensors acquire 360deg views of the environment and the projection from 3D world points to the image plane is approximated by a perspective model. This is a major advantage in structured environments because straight lines on specific surfaces are not deformed by the sensor, allowing the application of standard computer vision algorithms. Objects off the surface are distorted according to a complex projection model, but can be approximated by a simple wide angle perspective mapping. This is exploited here to develop a robust tracking system for autonomous robots using a 3D shape and color-based object model. The use of particle filters allows tracking to be done with 3D realistic motion models and tackling object occlusion, overlap and ambiguities. We show that the use of the perspective model is advantageous over more standard catadioptric projection models, since it renders a very good approximation to the true model, being simpler and more efficient to use, in particular with 3D particle filtering methods.

ICRA Conference 2006 Conference Paper

Inverse Reinforcement Learning with Evaluation

Valdinei Freire da Silva
Anna Helena Reali Costa
Pedro U. Lima

Reinforcement learning (RL) is a method that helps programming an autonomous agent through human-like objectives as reinforcements, where the agent is responsible for discovering the best actions to fulfil the objectives. Nevertheless, it is not easy to disentangle human objectives in reinforcement like objectives. Inverse reinforcement learning (IRL) determines the reinforcements that a given agent behaviour is fulfilling from the observation of the desired behaviour. In this paper we present a variant of IRL, which is called IRL with evaluation (IRLE) where instead of observing the desired agent behaviour, the relative evaluation between different behaviours is known by the access to an evaluator. We present also a solution for this problem under the assumption that a relative linear function that preserves the order assumed by the evaluator exists and that the evaluator evaluates policies instead of behaviours. This is posed as a linear feasibility problem, whose solution is well known. Results of simulations of a set of heterogeneous robots in a search and rescue scenario are presented to illustrate the method and the possibility to transfer the learned reinforcement function among robots

IROS Conference 2006 Conference Paper

RAPOSA: Semi-Autonomous Robot for Rescue Operations

Carlos F. Marques
João Cristóvão
Pedro U. Lima
João Frazão
M. Isabel Ribeiro
Rodrigo M. M. Ventura

This work describes a semi-autonomous robot for rescue operations, nicknamed RAPOSA (FOX in English). The robot was designed and built to operate in outdoor environments hostile to the human presence, such as debris resulting from the collapse of built structures, and is targeted to the tele-operated detection of potential survivors using a set of specific sensors whose information is transmitted to a remote human operator. RAPOSA's mechanical structure is composed of a main body and a front body, whose locomotion is supported on tracked wheels, allowing motion even when the robot is upside down. The front body has variable tilting capabilities, providing means to overcome edges higher than the robot main body (e. g. , when climbing a stair) and is also useful to grab the lower ground when only the main body has ground contact. This front body has one thermal camera and two webcameras installed. Additional sensors include gas, temperature and humidity sensors, web cams, light diodes, microphone and loudspeaker. The robot uses wireless communications, with an option for tethered operation. The tether carries both power and communications, with an access point on its end, and can also be used to suspend the robot inside a deep hole. Docking and undocking the robot to the tether is accomplished remotely by the operator with the help of a camera located inside the robot, and represents the most innovative feature of RAPOSA.

EAAI Journal 2003 Journal Article

A glass furnace operation system using fuzzy modelling and genetic algorithms for performance optimisation

João M. Pina
Pedro U. Lima

An architecture for the operation of a recuperative-type glass furnace is introduced in this paper. It is based on a hierarchical scheme, with two main parts: process optimisation and process modelling. Process optimisation is carried out by an expert controller, and uses genetic algorithms to solve a multiobjective optimisation problem. Process modelling is performed by a learning system, based on a fuzzy learning-by-examples algorithm. Results of real and simulated experiments with the glass manufacturing process are presented.

ICRA Conference 2002 Conference Paper

Petri Net Models of Robotic Tasks

Dejan Milutinovic
Pedro U. Lima

Introduces a robotic task model (RTM) based on Petri nets, that establishes a framework for task evaluation from qualitative and quantitative viewpoints, as well as a methodology for the implementation of robotic task coordination. A testbed for the evaluation of the RTM and the details of its implementation over a network of distributed task executors is described.

IROS Conference 2000 Conference Paper

Vision-based self-localization for soccer robots

Carlos F. Marques
Pedro U. Lima

In this paper, a method for robot self-localization based on a catadioptric omni-directional sensor is introduced. The method uses natural geometric landmarks of the environment. It is assumed that the robot moves on flat surfaces and straight lines can be identified in the surrounding environment image acquired by the catadioptric system. This omni-directional vision system is based on a camera plus a convex mirror designed to obtain (by hardware) the ground plane bird's eye view. Results from the application to a real robot moving on RoboCup soccer field and concerning the method's accuracy are presented.

ICRA Conference 1994 Conference Paper

Hierarchical Reinforcement Learning and Decision Making for Intelligent Machines

Pedro U. Lima
George N. Saridis

A methodology for performance improvement of intelligent machines based on hierarchical reinforcement learning is introduced. Machine decision making and learning are based on a cost function which includes reliability and a computational cost of algorithms at the three levels of the hierarchy proposed by Saridis. Despite this particular formalization, the methodology intends to be sufficiently general to encompass different types of architectures and applications. Novel contributions of this work include the definition of a cost function combining reliability and complexity, recursively improved through feedback, a hierarchical reinforcement learning and decision making algorithm which uses that cost function, and a methodology supported on information-based complexity for joint measure of algorithm cost and reliability. Results of simulations show the application of the formalism to intelligent robotic systems. >

IROS Conference 1990 Conference Paper

An architecture for the supervision of fuzzy controllers

Paulo J. Oliveira
Pedro U. Lima
João J. Sentieiro
R. S. Bravo
R. Galan
A. Jimenez

A new approach to the supervision of fuzzy controllers is presented. The supervision loop is designed to overcome some of the problems that remain unsolved, such as time-varying plants, high nonlinear plants or fine tuning of the linguistic terms given by the expert in a fuzzy controller. The supervision concept is based on the continuous adjustment of the mathematical functions used for the definition of the linguistic terms which describe the actions of the rules. The amount of adjustment is the result of a weighted combination of the results using two features observed in the control system output-the rise time and the overshoot.