Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems

Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams

As a step towards achieving autonomy in space exploration missions, we consider a cooperative robotics system consisting of a copter and a rover. The goal of the copter is to explore an unknown environment so as to maximize knowledge about a science mission expressed in linear temporal logic that is to be executed by the rover. We model environmental uncertainty as a belief space Markov decision process and formulate the problem as a two-step stochastic dynamic program that we solve in a way that leverages the decomposed nature of the overall system. We demonstrate in simulations that the robot team makes intelligent decisions in the face of uncertainty

Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors

In persistent missions, taking system’s health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discrete mission-level states. This in particular poses a challenge when the mission domain is partially observable and restricts the use of computationally expensive forward search methods. This paper presents a method that exploits a structure that exists in many health-aware planning problems and performs a two-layer planning scheme. The lower layer exploits the local linearization and Gaussian distribution assumption over vehicle-level states while the higher layer maintains a non-Gaussian distribution over discrete mission-level variables. This two-layer planning scheme allows us to limit the expensive online forward search to the mission-level states, and thus predict system’s behavior over longer horizons in the future. We demonstrate the performance of the method on a long duration package delivery mission using a quadrotor in a partially-observable domain in the presence of constraints and health/capability degradation

Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation

Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0391)United States. Office of Naval Research (Grant N00014-11-1-0688)National Science Foundation (U.S.) (Award IIS-1318392

Screening to Determine Prevalence of β-Thalassemia and Iron Deficiency Anemia Among Medical Students

According to the Thalassemia Federation of Pakistan, the mostly inherited disorder in Pakistan is β-thalassemia, which is characterized by a deficient, abnormal, or lack of β-globin chain synthesis and has a prevalence of 6%. The only method of controlling and preventing β-thalassemia is to increase awareness among students. This was an observational study using a random sampling technique. The Dow-Thalassemia awareness program recruited 915 medical students from the Dow Medical College (DMC) and Sindh Medical College (SMC) to voluntarily donate blood samples, which were analyzed by the naked eye single tube red cell osmotic fragility test (NESTROFT) and complete blood count and results were confirmed by high-performance liquid chromatography and analyzed using the NESTROFT. The samples were collected in 2012-2013. A total of 915 samples, out of these 390 samples, 390/915 (42.6%) samples were positive and complete blood count found 282 (72.3%) were positive for iron deficiency anemia. The remaining 108/390 (27.6%) were confirmed by high-performance liquid chromatography. Only 2.4 % subjects were positive for the β-thalassemia trait. Of 915 students, 57.4% of students were healthy, 39.2% had iron deficiency anemia, and 2.4% were carriers of the β-thalassemia trait. The overall prevalence of β-thalassemia was 38/915 (4.1%), which was lower than observed in previous studies. This study also demonstrated the NESTROFT can be used as a primary method of screening out healthy individuals, where approximately 50% require further screening for β-thalassemi

Perception-aware Autonomous Mast Motion Planning for Planetary Exploration Rovers

Highly accurate real-time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision-based systems to avoid hazards as well as plan safe routes. However, vision-based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision-based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this work, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared to traditional fixed-sensor configurations

Distributed Anonymity Based on Blockchain in Vehicular Ad Hoc Network by Block Size Calibrating

The network connectivity problem is one of the critical challenges of an anonymous server implementation in the VANET. The objective and main contribution of this paper are to assure the anonymity in VANET environments. In the proposed blockchain method, before packaging transactions into blocks, anonymity risk reduced through techniques such as k-anonymity, graph processing, dummy node, and silence period. This paper addresses the challenges of anonymous servers, such as update challenges and single point of failure, by exploiting append-only, distributed, and anonymity features. Although mounting the blockchain process with asymmetric cryptography solves the connectivity challenge, start-up delay and network overhead are severe. The significant feature of the proposed method solves this delay challenge by aggregating many transactions into a block and fixing constraint range of multicasting blocks. Also, aggregating transactions of various end-users into a block preserves the path anonymity. The asymmetric cryptography with ring public and private keys protects the identity anonymity as well as unlinkability. The robust anonymity mechanism existence and the traceability of all transactions constitute the main advantages of the proposed method. The simulation is running by the python to evaluate blockchain performance in VANET with connectivity failure and rapidly changing topology. The results indicate the stabilization of the proposed method in the VANET environment