Dali Sun

ARMO: Adaptive road map optimization for large robot teams

Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map configuration according to environmental constraints (including human whereabouts) and the demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is described by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.

Decentralized SLAM for pedestrians without direct communication

We consider the problem of Decentralized Simultaneous Localization And Mapping (DSLAM) for pedestrians in the context of Urban Search And Rescue (USAR). In this context, DSLAM is a challenging task. First, data exchange fails due to cut off communication links. Second, loop-closure is cumbersome due to the fact that firefighters will intentionally try to avoid performing loops when facing the reality of emergency response, e.g. while they are searching for victims. In this paper, we introduce a solution to this problem based on the non-selfish sharing of information between pedestrians for loop-closure. We introduce a novel DSLAM method which is based on data exchange and association via RFID technology, not requiring any radio communication. The approach has been evaluated in both semi-indoor and outdoor environments. The presented results show that sharing information between single pedestrians allows to optimize globally their individual paths, even if they are not able to communicate directly.

Multi-robot Range-Only SLAM by Active Sensor Nodes for Urban Search and Rescue

To jointly map an unknown environment with a team of autonomous robots is a challenging problem, particularly in large environments, as for example the area of devastation after a disaster. Under such conditions standard methods for Simultaneous Localization And Mapping (SLAM) are difficult to apply due to possible misinterpretations of sensor data, leading to erroneous data association for loop closure. We consider the problem of multi-robot range-only SLAM for robot teams by solving the data association problem with wireless sensor nodes that we designed for this purpose. The memory of these nodes is utilized for the exchange of map data between multiple robots, facilitating loop-closures on jointly generated maps. We introduce RSLAM, which is a variant of FastSlam, extended for range-only measurements and the multi-robot case. Maps are generated from robot odometry and range estimates, which are computed from the RSSI (Received Signal Strength Indication). The proposed method has been extensively tested in USARSim, which serves as basis for the Virtual Robots competition at RoboCup, and by real-world experiments with a team of mobile robots. The presented results indicates that the approach is capable of building consistent maps in presence of real sensor noise, as well as to improve mapping results of multiple robots by data sharing.

Behavior-based multi-robot collision avoidance

Autonomous robot teams that simultaneously dispatch transportation tasks are playing a more and more important role in the industry. In this paper we consider the multi-robot motion planning problem in large robot teams and present a decoupled approach by combining decentralized path planning methods and swarm technologies. Instead of a central coordination, a proper behavior which is directly selected according to the context is used by the robot to keep cooperating with others and to resolve path collisions. We show experimentally that the quality of solutions and the scalability of our method are significantly better than those of conventional decoupled path planning methods. Furthermore, compared to conventional swarm approaches, our method can be widely applied in large-scale environments.

Outline of an empirical study on the effects of emotions on strategic behavior in virtual emergencies

The applicability of appropriate coping strategies is important in emergencies or traumatic experiences such as car accidents or human violence. In this context, emotion regulation and decision making are relevant. However, research on human reactions to traumatic experiences is very challenging and most existing research uses retrospective assessments of these variables of interest. Thus, we are currently developing and evaluating novel methods to investigate human behavior in cases of emergency. Virtual reality scenarios of emergencies are employed to enable an immersive interactive engagement (e.g., dealing with fire inside a building) based on the modification of Valves popular Source™ 2007 game engine. This paper presents our ongoing research project, which aims at the empirical investigation of human strategic behavior under the influence of emotions while having to cope with virtual emergencies.

The impact of pre-existing anxiety on affective and cognitive processing of a Virtual Reality analogue trauma

Dysfunctional processing of traumatic events may be in particular related to high trait anxiety as a pre-traumatic risk factor for the development of post-traumatic stress disorder (PTSD). However, as this has rarely been investigated in prospective, experimental studies, we aimed to analyse the association between high trait anxiety and affective as well as cognitive processing of stress using a new prospective Virtual Reality analogue trauma paradigm to overcome limitations of retrospective or current analogue designs. Individuals with high and low trait anxiety (N = 80) were exposed to a multi-sensory Virtual Reality emergency scenario while psychophysiological stress response, emotion regulation and intrusive memories were assessed. Our results showed that high trait anxiety individuals display increased (i) subjective stress responses, (ii) emotion dysregulation and (iii) intrusive memories upon VR analogue trauma exposure. In particular, our sample of high trait anxiety individuals displayed limited access to different emotion regulation strategies as well as increased worry and rumination regarding perceived intrusive memories. Considering the complex interplay of multiple risk factors, our findings suggests that peri-traumatic affective processing seems to mediate high trait anxiety and post-traumatic intrusive memories thereby pointing out the central role of peri-traumatic processes for intrusion development. In addition, HA as a modulating pre-traumatic risk factor might further increase the risk of later dysfunctional processing of an analogue trauma by interacting with factors of affective processing during analogue trauma exposure. Implications of these findings which may contribute to a higher risk to develop PTSD are discussed.

Psychophysiological reactivity, coping behaviour and intrusive memories upon multisensory Virtual Reality and Script-Driven Imagery analogue trauma: A randomised controlled crossover study

Highlights • Real-time assessment of peri- and post-traumatic risk factors for stress disorders.• Experimental stress induction by a VR and SDI multisensory analogue trauma paradigm.• VR and SDI induced trauma-like symptoms in a randomised controlled crossover design.• VR induced more trauma-like symptoms than SDI.• Both paradigms offer real-time modelling of stress-associated disorders.

Towards effective localization in dynamic environments

Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a particle filter, coupled with a distance filter and a scan matching method, which achieves a more robust localization against dynamic obstacles. For the mapping problem we use an extended sensor model which results in an effective and precise map update effect. We compare our approach against other localization methods and evaluate the impact the map update effect has on the localization in dynamic environments.