Abstract--Balancing the trade-off between safety and efficiency paths in uncertain environments, especially under nonconvex and is of significant importance for path planning under uncertainty. The initial paths are often of poor Many risk-aware path planners have been developed to explicitly quality as well. Therefore, we propose the NR-RRT algorithm limit the probability of collision to an acceptable bound in to rapidly find near-optimal solutions with guaranteed bounded uncertain environments. It utilizes an informed bidirectional search strategy after uncertainties are usually assumed to make the problem tractable having past experiences in those challenging environments. These assumptions limit the generalization RRT can be applied in not only seen but also unseen uncertain and application of path planners in real-world implementations. However, the algorithm cannot handle entirely unseen In this article, we propose to apply deep learning methods to environments that contain new or additional obstacles. In future the sampling-based planner, developing a novel risk bounded research, we will address the problem of planning under robot near-optimal path planning algorithm named neural risk-aware model uncertainty. Specifically, a deterministic risk contours map is Index Terms--Planning under uncertainty, Sampling-based maintained by perceiving the probabilistic nonconvex obstacles, path planning, Learning from demonstration. and a neural network sampler is proposed to predict the next most-promising safe state.

Online Appointment Scheduling Software At ITFrontDesk we use innovative IVR technology that's been created to automate the front desk of many different types of businesses. Our valuable products have been created to keep your staff focused on other important tasks besides scheduling appointments, calling for appointment reminders, event reservations, and message broadcasting.

An internet-based presence for your web-based business goes a long way past setting up a site that contains the name of your organization and contact subtleties. It includes making a virtual place of business where individuals can track down helpful data on your site and associate with you through online entertainment organizations. Computerized strength has made it essential for additional individuals to depend on the web to search for the items and administrations that they need. Any web-based business that doesn't successfully utilize this stage chances losing new worthwhile open doors. The web is generally accessible and can feature your business the entire day.

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate the static or continuous planning towards performance, resource management, or functional goals (e.g., arriving at the destination, managing fuel fuel consumption). Existing planning algorithms assume non-adversarial settings; a least-cost plan is developed based on available environmental information (i.e., the input instance). Yet, it is unclear how such algorithms will perform in the face of adversaries attempting to thwart the planner. In this paper, we explore the security of planning algorithms used in cyber- and cyber-physical systems.

Space missions are characterized by long distances, difficult or unavailable communication and high operating costs. Moreover, complexity has been constantly increasing in recent years. For this reason, improving the autonomy of space operators is an attractive goal to increase the mission reward with lower costs. This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. Given a set of activities, it is responsible for determining the starting time of each activity according to their priority, order constraints, and resource consumption. The presented scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology.

Learning how a large software project works is essential if you want to contribute to it. However, when faced with the task it can be hard to know where to start. These are great tips, both for working on open and closed source projects. Does anybody have any others?

Monte Carlo Tree Search (MCTS) is a popular tree-based search strategy within the framework of reinforcement learning (RL), which estimates the optimal value of a state and action by building a tree with Monte Carlo simulation. It has been widely used in sequential decision makings, including scheduling problems, inventory, production management, and real-world games, such as Go, Chess, Tic-tac-toe and Chinese Checkers. See Browne et al. (2012), Fu (2018) and Świechowski et al. (2021) for thorough overviews. MCTS uses little or no domain knowledge and self learns by running more simulations. Many variations have been proposed for MCTS to improve its performance. In particular, deep neural networks are combined into MCTS to achieve a remarkable success in the game of Go (Silver et al. 2016, 2017). A basic MCTS is to build a game tree from the root node in an incremental and asymmetric manner, where nodes correspond to states and edges correspond to possible state-action pairs.

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

Developing controllers for obstacle avoidance between polytopes is a challenging and necessary problem for navigation in tight spaces. Traditional approaches can only formulate the obstacle avoidance problem as an offline optimization problem. To address these challenges, we propose a duality-based safety-critical optimal control using nonsmooth control barrier functions for obstacle avoidance between polytopes, which can be solved in real-time with a QP-based optimization problem. A dual optimization problem is introduced to represent the minimum distance between polytopes and the Lagrangian function for the dual form is applied to construct a control barrier function. We validate the obstacle avoidance with the proposed dual formulation for L-shaped (sofa-shaped) controlled robot in a corridor environment. We demonstrate real-time tight obstacle avoidance with non-conservative maneuvers on a moving sofa (piano) problem with nonlinear dynamics.