This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planning for evacuations, robotic navigations, and path planning for autonomous vehicles. We propose an architecture based on GANs to recommend accurate and reliable paths for navigation applications. The proposed system can use crowd-sourced data to learn the trajectories and infer new ones. The system provides users with generated paths that help them navigate from their local environment to reach a desired location. As a use case, we experimented with the proposed method in support of a wayfinding application in an indoor environment. Our experiments assert that the generated paths are correct and reliable. The accuracy of the classification task for the generated paths is up to 99 and the quality of the generated paths has a mean opinion score of 89 .
Chen @cite_20 propose a path planning system for emergency guidance in indoor environments based on IoT technologies. They use the statistical properties of mobility of groups of people to provide an individualized path for each group. In this work, a graph of the corridors, doors, and exits is optimized to minimize the total evacuation time for all groups. They implemented their work by utilizing the iBeacon technology and smartphones.
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This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework. In an emergency, wireless network sensors combined with a navigation algorithm could help safely guide people to a building exit while helping them avoid hazardous areas. We propose a distributed navigation algorithm for emergency situations. At normal time, sensors monitor the environment. When the sensors detect emergency events, our protocol quickly separates hazardous areas from safe areas, and the sensors establish escape paths. Simulation and implementation results show that our scheme achieves navigation safety and quick convergence of the navigation directions. We based our protocol on the temporally ordered routing algorithm for mobile ad hoc networks. TORA assigns mobile nodes temporally ordered sequence numbers to support multipath routing from a source to a specific destination node Exit paths in buildings are designed to minimise evacuation time when the building is at full capacity. We present an evacuation support system which does this regardless of the number of evacuees. The core concept is to even-out congestion in the building by diverting evacuees to less-congested paths in order to make maximal usage of all accessible routes throughout the entire evacuation process. The system issues a set of flow-optimal routes using a capacity-constrained routing algorithm which anticipates evolutions in path metrics using the concept of "future capacity reservation". In order to direct evacuees in an intuitive manner whilst implementing the routing algorithm's scheme, we use dynamic exit signs, i.e. whose pointing direction can be controlled. To make this system practical and minimise reliance on sensors during the evacuation, we use an evacuee mobility model and make several assumptions on the characteristics of the evacuee flow. We validate this concept using simulations, and show how the underpinning assumptions may limit the system's performance, especially in low-headcount evacuations. In this paper we describe a novel path planning approach for mobile robots operating in indoor environments. In such scenarios, robots must be able to maneuver in crowded spaces, partially filled with static and dynamic obstacles (such as people). Our approach produces smooth, complex maneuvers over large distances through the use of an anytime graph search algorithm applied to a novel multi-resolution state lattice, where the resolution is adapted based on both environmental characteristics and task characteristics. In addition, we present a novel approach for generating fast globally optimal trajectories in constrained spaces (i.e. rooms connected via doors and hallways). This approach exploits offline precomputation to provide extremely efficient online performance and is applicable to a wide range of both indoor and outdoor navigation scenarios. By combining an anytime, multi-resolution lattice-based search algorithm with our precomputation technique, globally optimal trajectories in up to four dimensions (2D position, heading and velocity) are obtained in real-time. The evacuation of a building is a challenging problem, since the evacuees most of the times do not know or do not follow the optimal evacuation route. Especially during an ongoing hazard present in the building, finding the best evacuation route becomes harder as the conditions along the paths change in the course of the evacuation procedure. In this paper we propose a distributed system that will compute the best evacuation routes in real-time, while a hazard is spreading inside the building. The system is composed of a network of decision nodes and sensor nodes, positioned in specific locations inside the building. The recommendations of the decision nodes are computed in a distributed manner, at each of the decision nodes, which then communicate them to evacuees or rescue personnel located in their vicinity. We evaluate our proposed system in various emergency scenarios, using a multi-agent simulation platform for Building Evacuation. Our results indicate that the presence of the system improves the outcome of the evacuation with respect to the evacuation time and the injury level of the evacuees. Opportunistic communications (oppcomms) use low-cost human wearable mobile nodes allowing the exchange of packets at a close range of a few to some tens of meters with limited or no infrastructure. Typically cheap pocket devices which are IEEE 802.15.4-2006 compliant can be used and they can communicate at 2m to 10m range, with local computational capabilities and some local memory. In this paper we consider the application of such devices to emergency situations when other means of communication have broken down. This paper evaluates whether oppcomms can improve the outcome of emergency evacuation in directing civilians safely. We describe an autonomous emergency support system (ESS) based on oppcomms to support evacuation of civilians in a built environment such as a building or supermarket. The proposed system uses a fixed infrastructure of sensor nodes (SNs) to monitor the environment. Hazard information obtained via SNs is disseminated to the individuals, and they spread among the people who are located in this built environment using oppcomm devices carried by these people. The information received by these people can then guide them safely to the exits as the emergency situation evolves over time. We evaluate our scheme using a distributed multi-agent building evacuation simulator (DBES) in the context of evacuation scenarios of a multi-storey office building in the presence of a fire that is spreading. The results show the degree of improvement that the oppcomms can offer. c � 2011 Published by Elsevier Ltd. In this paper we describe a new Bayesian estimation approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. When somebody walks within a constrained area such as a building, then even noisy and drift-prone odometry measurements can give us information about features like turns, doors, and walls, which we can use to build a form of a map of the explored area, especially when these features are revisited over time. Our initial results for our novel scheme which we call "FootSLAM" are very surprising in that true SLAM with stable relative positioning accuracy of 1-2 meters for pedestrians is indeed possible based on inertial sensors alone without any prior known building indoor layout. Furthermore, the 2D maps obtained even for just 10 minutes of walking converge to a good approximation of the true layout forming the basis for future automated collaborative mapping of buildings. Information systems that provide decision support in an efficient and timely manner can prove beneficial for emergency response operations. In this paper we propose the use of a system that provides movement decision support to evacuees by directing them through the shortest or less hazardous routes to the exit and evaluate it with a specialised software platform that we have developed for simulation of disasters in buildings. The system operates in a distributed manner, and computes the best evacuation routes in real-time while a hazard is spreading inside the building. It is composed of a network of decision nodes and sensor nodes, positioned in specific locations inside the building. The recommendations of the decision nodes are computed in a distributed manner, at each of the decision nodes, which then communicate them to evacuees or rescue personnel located in their vicinity. We use a multi-agent simulation platform for Building Evacuation that we developed, in order to evaluate our proposed system in various emergency scenarios. Our simulation results show that the overall outcome of the evacuation procedure is improved when the decision support system is in operation. Given a large city represented by nodes and arcs, with a subset of these nodes having people required to be evacuated in a disaster situation, and a subset of destination nodes where the people may be taken for safety, the evacuation route planner finds routes in this network to evacuate the people in minimum possible time. Evacuation route planning in pre-known disasters such as hurricanes is of utmost importance for civic authorities. Computing such evacuation routes in a large graph with road infrastructure constraints such as road capacities and travelling times can be challenging. The Capacity Constrained Route Planner (CCRP) is a well studied heuristic algorithm proposed by Lu, George and Shekhar for solving this problem. It uses shortest path computations as a basis for finding and scheduling possible evacuation paths in the graph. However, the algorithm fails to scale to very large graphs. Other algorithms based on CCRP like CCRP++ and Incremental Data Structure based CCRP have been proposed to scale them to larger graphs. In this paper, we analyze these algorithms from performance perspective and suggest a faster algorithm by extending the CCRP algorithm that avoids recomputing partial paths. We have carried out experiments on various graph structures which show a vast improvement in runtime without impacting the optimality of CCRP results. For instance, for the city of Oldenburg with 382285 people, 6105 nodes and 7034 edges, our algorithm produced an evacuation plan in 2.6 seconds as compared to 123.8 seconds by CCRP and 9.3 seconds by CCRP++. We present a novel approach for real-time and proactive navigation in crowded environments such as event spaces and urban areas where many people are moving to their destinations simultaneously. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. Our approach tries to detect future congestion by using a spatio-temporal statistical method that predicts people flow. When future congestion is detected, our approach creates an optimal navigation plan based on "what-if" simulations, which accounts for the effect of total people flow change caused by navigation. We experimentally compare the spatio-temporal statistical method with the conventional matrix factorization based approach using a real data set. We also demonstrate the effectiveness of our navigation approach by computer simulation using artificial people-flow data. Emergency response operations can benefit from the use of information systems that reduce decision making time and facilitate co-ordination between the participating units. We propose the use of two such systems and evaluate them with a specialised software platform that we have developed for simulation of disasters in buildings. The first system provides movement decision support to evacuees by directing them through the shortest or less hazardous routes to the exit. It is composed of a network of decision nodes and sensor nodes, positioned at specific locations inside the building. The recommendations of the decision nodes are computed in a distributed manner and communicated to the evacuees or rescue personnel in their vicinity. The second system uses wireless-equipped robots that move inside a disaster area and establish a network for two-way communication between trapped civilians and rescuers. They are autonomous and their goal is to maximise the number of civilians connected to the network. We evaluate both proposed information systems in various emergency scenarios, using the specialised simulation software that we developed. Although GPS has been considered a ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. While a number of technologies have been proposed for indoor localization, they are isolated efforts that are way from a true ubiquitous localization system. A ubiquitous indoor positioning system is envisioned to be deployed on a large scale worldwide, with minimum overhead, to work with heterogeneous devices, and to allow users to roam seamlessly from indoor to outdoor environments. Such a system will enable a wide set of applications including worldwide seamless direction finding between indoor locations, enhancing first responders' safety by providing anywhere localization and floor plans, and providing a richer environment for location-aware social networking applications. We describe an architecture for the ubiquitous indoor positioning system (IPS) and the challenges that have to be addressed to materialize it. We then focus on the feasibility of automating the construction of a worldwide indoor floor plan and fingerprint database which, as we believe, is one of the main challenges that limit the existence of a ubiquitous IPS system. Our proof of concept uses a crowd-sourcing approach that leverages the embedded sensors in today's cell phones as a worldwide distributed floor plan generation tool. This includes constructing the floor plans and determining the areas of interest (corridors, offices, meeting rooms, elevators, etc). The cloud computing concepts are also adopted for the processing and storage of the huge amount of data generated and requested by the system's users. Our results show the ability of the system to construct an accurate floor plan and identify the areas of interest with more than 90 accuracy. We also identify different research directions for addressing the challenges of realizing a true ubiquitous IPS system. This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area. Consider a setting where robots must visit sites represented as nodes in a graph, but each robot may fail when traversing an edge. The goal is to find a set of paths for a team of robots which maximizes the expected number of nodes collectively visited, while guaranteeing that the paths satisfy a notion of “independence” formalized by a matroid (e.g. limits on team size, number of visits to regions), and that the probabilities that each robot survives to its destination are above a given threshold. We call this problem the Matroid Team Surviving Orienteers (MTSO) problem, which has broad applications such as environmental monitoring in risky regions and search and rescue in dangerous conditions. We present the MTSO formally and detail numerous examples of matroids in a path planning context. We then propose an approximate greedy algorithm for selecting a feasible set of paths and prove that the value of the output is within a factor p s p s + λ of the optimum, where p s is the per-robot survival probability threshold and 1 λ ≤ 1 is the approximation factor of an oracle routine for the well known orienteering problem. We demonstrate the efficiency of our approach by applying it to a scenario where a team of robots must gather information while avoiding pirates in the Coral Triangle. Humans navigate crowded spaces such as a university campus by following common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new target tracking or trajectory forecasting methods that can take full advantage of these rules, we need to have access to better data in the first place. To that end, we contribute a new large-scale dataset that collects videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real world outdoor environment such as a university campus. Moreover, we introduce a new characterization that describes the “social sensitivity” at which two targets interact. We use this characterization to define “navigation styles” and improve both forecasting models and state-of-the-art multi-target tracking–whereby the learnt forecasting models help the data association step. This paper presents an Internet of Things (IoT) platform for a smart building which provides human care services for occupants. The individual health profiles of the occupants are acquired by the IoT-based smart building, which uses the accumulated knowledge of the occupants to provide better services. To ensure the thermal comfort of the occupants inside the building, we propose a dynamic thermal model of occupants. This model is based on the heat balance equation of human body and thermal characteristics of the occupants. We implement this model in two smart building models with heaters controlled by a temperature and thermal comfort index using MATLAB Simulink®. The simulation results show that the thermal comfort-based control is more effective to maintaining occupants’ thermal satisfaction and is therefore recommended for use providing human care services using IoT platforms in smart buildings. We present a heuristic-based search method for path planning in shared human-robot control scenarios in which the robot should adhere to specific motion constraints imposed by the human's control interface. This approach to path planning gives special consideration to kinematic and dynamic constraints introduced to reconcile discrepancies between the control space of the user and the control space of the robot. The resulting paths more closely mirror paths produced by users of the same interface; which is helpful, for example, when inferring human intent or for control sharing. Our first insight is to develop a hierarchical finite state machine describing the constrained state space, state transitions and associated costs. We then use this definition to embed the constraints of the interface into our heuristic planning algorithm, named C*, with simple modifications to the A* D* family of graph search algorithms. This approach allows us to maintain powerful theoretical guarantees such as complexity and completeness. In this paper, we ground our augmented path planning algorithm with an implementation on a robotic wheelchair system and a Sip-and-Puff interface. We demonstrate that the new approach produces paths and control signals that more closely resemble user-generated data and can easily be incorporated into real hardware systems. This paper describes a robust algorithm for mobile robot formations based on the Voronoi Fast Marching path planning method. This is based on the propagation of a wave throughout the model of the environment, the wave expanding faster as the wave's distance from obstacles increases. This method provides smooth and safe trajectories and its computational efficiency allows us to maintain a good response time. The proposed method is based on a local-minima-free planner; it is complete and has an �1�:�J�; complexity order whereJ is the number of cells of the map. Simulation results show that the proposed algorithm generates good trajectories. We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method. This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present ; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.