Robot-assisted discovery of evacuation routes in emergency scenarios

Abstract — When an emergency occurs within a building, it is crucial to guide victims towards emergency exits or human responders towards the locations of victims and hazards. The objective of this work is thus to devise distributed algorithms that allow agents to dynamically discover and maintain short evacuation routes connecting emergency exits to critical cells in the area. We propose two Evacuation Route Discovery mechanisms, Agent2Tag-ERD and Tag2Tag-ERD, and show how they can be seamlessly integrated with existing exploration algorithms, like Ants, MDFS and Brick&Mortar. We then examine the interplay between the tasks of area exploration and evacuation route discovery; our goal is to assess whether the exploration algorithm influences the length of evacuation paths and the time that they are first discovered. Finally, we perform an extensive simulation to assess the impact of the area topology on the quality of discovered evacuation paths. I

Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i.e., every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.Comment: This paper is accepted by AAMAS 2023. The source code can be found in https://github.com/yang-xy20/async_mapp

Paikannus- ja kartoitusteknologiat autonomisissa laitteissa

Itsenäiset mobiilirobotit ja autonomiset ajoneuvot tarvitsevat kyvyn paikantaa itsensä ja kartoittaa ympäristönsä. Mobiilirobottien ja autonomisen liikenteen yleistyminen vaatii toimivat ja luotettavat paikannus- ja kartoitusmenetelmät, koska ne vastaavat laitteiden turvallisuudesta. Erilaisia paikannus- ja kartoitusteknologioita mobiilirobottien ja autonomisten ajoneuvojen käyttöön on monia. Yksikään teknologioista ei ole yksiselitteisesti luotettavin. Tavoitteena on löytää teknologioista hyviä ja huonoja puolia, joiden perusteella teknologioiden soveltuvuutta arvioidaan. Lisäksi SLAM-teknologioiden tulevaisuutta tarkastelemalla voidaan tehdä päätelmiä erilaisista lähestymistavoista. Vertailun perimmäisenä tarkoituksena on löytää ja esitellä vaihtoehtoisia teknologioita paikannukseen ja kartoitukseen. SLAM-teknologioiden tutkiminen kirjallisuuden avulla tuo oman haasteensa tutkimuksen luotettavuuteen. Akateemisten tekstien lisäksi tutkimustyöhön käytetään myös kaupallisia lähteitä. Monet tarkasteluun valikoituneista teknologioista ovat jo niin kypsiä paikannus- ja kartoitusmenetelmiksi, että niistä löytyy kaupallisia toteutuksia. Paikannukseen ja kartoitukseen käytetään yleisesti lidar-sensoreita ja kameroita, jotka ovat teknologioina keskenään hyvin erilaisia, mutta molempia käytetään autonomisissa laitteissa. Lidarin käyttö autonomisissa ajoneuvoissa jakaa mielipiteitä, koska sen käyttöön liittyy haasteita dynaamisen ympäristön ja hinnan kanssa. Esimerkiksi Tesla pyrkii käyttämään kameroita lidarin sijasta. Paikantamiseen ja kartoittamiseen löytyy myös muita teknologioita, esimerkiksi radar- tai RFID-sensoreiden avulla. Radar on jo käytössä ajoneuvojen mukautuvissa vakionopeudensäätimissä, koska se pystyy havaitsemaan esteitä pitkältä kantamalta sankankin sumun lävitse. Paikantamiseen voi käyttää myös akustiikkaa tai verkkoyhteyksiä, mutta näiden lähestymistapojen tarkkuus on muita heikompi. Näillekin teknologioille on kuitenkin olemassa käyttökohteita myös autonomisten laitteiden ulkopuolella. Tulevaisuudessa paikantamiseen ja kartoittamiseen tullaan tarvitsemaan enemmän laskentatehoa, kun olemassa olevat teknologiat kehittyvät tarkemmiksi ja luotettavammiksi. Kehitystä vaaditaan siis myös ympäröivissä teknologioissa. Esimerkiksi analogiset prosessorit matriisilaskentaan voivat olla yleisempiä tulevaisuudessa. Matriisilaskenta on tärkeässä osassa erityisesti koneoppimisessa, ja koneoppiminen toimii hyvin esimerkiksi kameroiden kanssa. Esitellyistä teknologioista löydettyjen hyvien ja huonojen puolien avulla perustellaan kameroiden olevan paras teknologia autonomisen liikenteen ja mobiilirobottien tarpeisiin. Tähän vaikuttavat ensisijaisesti kameroiden halpa hinta ja joustavuus, jotka tekevät kameroista houkuttelevan vaihtoehdon. Kamerat tarjoavat ympäristöstään myös paljon sellaisia tietoja, joita muilla teknologioilla ei voida kerätä, kuten esimerkiksi objektien värejä tai pinnanlaatuja. Kamerat eivät kuitenkaan ole täydellinen teknologia ja kehitystyölle on vielä varaa. Kameroiden käyttäminen ei tietenkään sulje pois muiden teknologioiden käyttömahdollisuutta niiden rinnalla

Long-term RFID SLAM using Short-Range Sparse Tags

While on the path forward to the long-term or lifelong robotics, one of the most important capabilities is to have a reliable localization and mapping module. Data association and loop detection play critical roles in the localization and mapping problem. By utilizing the radio frequency identification (RFID) technology, these problems can be solved using the extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) with the tag information. But one of the critical barriers to the long-term SLAM is the overconfidence issue. In this paper, we focus on solving the overconfidence issue, which is introduced by the linearization errors. An Unit Circle Representation (UCR) is proposed to diminish the error in the prediction stage and a Correlation Coefficient Preserved Inflation (CCPI) is developed to recover the overconfidence issue in the update stage. Based on only odometry and sparse short-range RFID data, the proposed method is capable to compensate the linearization errors in both simulation and real experiments

Genetic stigmergy: Framework and applications

Stigmergy has long been studied and recognized as an effective system for self-organization among social insects. Through the use of chemical agents known as pheromones, insect colonies are capable of complex collective behavior often beyond the scope of an individual agent. In an effort to develop human-made systems with the same robustness, scientists have created artificial analogues of pheromone-based stigmergy, but these systems often suffer from scalability and complexity issues due to the problems associated with mimicking the physics of pheromone diffusion. In this thesis, an alternative stigmergic framework called \u27Genetic Stigmergy\u27 is introduced. Using this framework, agents can indirectly share entire behavioral algorithms instead of pheromone traces that are limited in information content. The genetic constructs used in this framework allow for new avenues of research, including real-time evolution and adaptation of agents to complex environments. As a nascent test of its potential, experiments are performed using genetic stigmergy as an indirect communication framework for a simulated swarm of robots tasked with mapping an unknown environment. The robots are able to share their behavioral genes through environmentally distributed Radio-Frequency Identification cards. It was found that robots using a schema encouraging them to adopt lesser used behavioral genes (corresponding with novelty in exploration strategies) can generally cover more of an environment than agents who randomly switch their genes, but only if the environmental complexity is not too high. While the performance improvement is not statistically significant enough to clearly establish genetic stigmergy as a superior alternative to pheromonal-based artificial stigmergy, it is enough to warrant further research to develop its potential

TEAMLOG in Action:a Case Study in Teamwork

This article presents a case study of a theoretical multi-agent system designed to clean up ecological disasters. It focuses on the interactions within a heterogeneous team of agents, outlines their goals and plans, and establishes the necessary distribution of information and commitment throughout the team, including its sub-teams. These aspects of teamwork are presented in the TEAMLOG formalism [20], based on multi-modal logic, in which collective informational and motivational attitudes are first-class citizens. Complex team attitudes are justified to be necessary in the course of teamwork. The article shows how to make a bridge between theoretical foundations of TEAMLOG and an application and illustrates how to tune TEAMLOG to the case study by establishing sufficient, but still minimal levels for the team attitudes