Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

Obstacle Avoidance and Proscriptive Bayesian Programming

Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac

Implementation of the autonomous functionalities on an electric vehicle platform for research and education

Self-driving cars have recently captured the attention of researchers and car manufacturing markets. Depending upon the level of autonomy, the cars are made capable of traversing from one point to another autonomously. In order to achieve this, sophisticated sensors need to be utilized. A complex set of algorithms is required to use the sensors data in order to navigate the vehicle along the desired trajectory. Polaris is an electric vehicle platform provided for research and education purposes at Aalto University. The primary focus of the thesis was to utilize all the sensors provided in Polaris to their full potential. So that, essential data from each sensor is made available to be further utilized either by a specific automation algorithm or by some mapping routine. For any autonomous robotic system, the first step towards automation is localization. That is to determine the current position of the robot in a given environment. Different sensors mounted over the platform provide such measurements in different frames of reference. The thesis utilizes the GPS based localization solution combined with the LiDAR data and wheel odometry to perform autonomous tasks. Robot Operating System is used as the software development tool in thesis work. Autonomous tasks include the determination of the global as well as the local trajectories. The endpoints of the global trajectories are dictated by the set of predefined GPS waypoints. This is called target-point navigation. A path needs to be planned that avoids all the obstacles. Based on the planned path, a set of velocity commands are issued by the embedded controller. The velocity commands are then fed to the actuators to move the vehicle along the planned trajectory

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Neural Network based Controller for High Speed Vehicle following Predetermined Path

The actual integration of automated control systems in vehicles such as Anti-lock Braking Systems (ABS) or Traction Control System (TCS) has proved to increase road safety and improve driver's comfort. Since most of the accidents are attributed to the fault of the driver, automated control systems in vehicle safety technology may dramatically better road safety by improving driver's performance. This thesis presents an enhanced and improved autonomous intelligent cruise control systems with obstacle collision avoidance integrated with path following/lane keeping. Obstacle collision avoidance is the ability to avoid obstaclesthat are in the vehicle's path, without causing damage to the obstacle or vehicle. Path following/lane keeping is the ability to follow the vehicle's path and keeping in its lane, as accurately as possible. The idea is to have a vehicle that drives by itself and avoids obstacles in the real world. Every instant, the vehicle decides by itself how to modify its direction according to its environment. This thesis demonstrates Gaussian functions and multi-objective cost function employed alongside with the Neural Network and optimal preview controller for control of the position of the vehicle to move while avoiding collision with obstacles. Each obstacle is represented independent of the others as a bell-shaped hump by the Gaussian functions which serve as an obstacle recognition system. Multi-objective cost function is formed for the planning strategy to generate, evaluate and select plans so that the vehicle can select which direction to move. Neural Network and optimal preview steering control are utilized to control a full linear steering model of a vehicle so as to increase path following accuracy. Optimal preview control is capable to portray the driver's vision of the path and process the knowledge while Neural Network controller has the ability to 'learn' from past errors and adjust the network to obtain specific target output. In this thesis, a MATLAB simulation environment was created to simulate the ability of a vehicle to avoid obstacles that are in the vehicle's path. Simulated obstacle avoidance has confirmed the capability of a vehicle to precisely avoid collision with obstacles while traveling on high speed along its predetermined path. i