Solving the Direction Field for Discrete Agent Motion

Models for pedestrian dynamics are often based on microscopic approaches allowing for individual agent navigation. To reach a given destination, the agent has to consider environmental obstacles. We propose a direction field calculated on a regular grid with a Moore neighborhood, where obstacles are represented by occupied cells. Our developed algorithm exactly reproduces the shortest path with regard to the Euclidean metric.Comment: 8 pages, 4 figure

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

A Simple Reactive Obstacle Avoidance Algorithm and Its Application in Singapore Harbor

Autonomous surface craft (ASC) are increasingly attractive as a means for performing harbor operations including monitoring and inspection. However, due to the presence of many fixed and moving structures such as pilings, moorings, and vessels, harbor environments are extremely dynamic and cluttered. In order to move autonomously in such conditions ASC’s must be capable of detecting stationary and moving objects and plan their paths accordingly. We propose a simple and scalable online navigation scheme, wherein the relative motion of surrounding obstacles is estimated by the ASC, and the motion plan is modified accordingly at each time step. Since the approach is model-free and its decisions are made at a high frequency, the system is able to deal with highly dynamic scenarios. We deployed ASC’s in the Selat Pauh region of Singapore Harbor to test the technique using a short-range 2-D laser sensor; detection in the rough waters we encountered was quite poor. Nonetheless, the ASC’s were able to avoid both stationary as well as mobile obstacles, the motions of which were unknown a priori. The successful demonstration of obstacle avoidance in the field validates our fast online approach.Massachusetts Institute of Technology. Singapore-MIT Alliance in Research and Technology (SMART

A vision for global monitoring of biological invasions

Managing biological invasions relies on good global coverage of species distributions. Accurate information on alien species distributions, obtained from international policy and cross-border co-operation, is required to evaluate trans-boundary and trading partnership risks. However, a standardized approach for systematically monitoring alien species and tracking biological invasions is still lacking. This Perspective presents a vision for global observation and monitoring of biological invasions. We show how the architecture for tracking biological invasions is provided by a minimum information set of Essential Variables, global collaboration on data sharing and infrastructure, and strategic contributions by countries. We show how this novel, synthetic approach to an observation system for alien species provides a tangible and attainable solution to delivering the information needed to slow the rate of new incursions and reduce the impacts of invaders. We identify three Essential Variables for Invasion Monitoring; alien species occurrence, species alien status and alien species impact. We outline how delivery of this minimum information set by joint, complementary contributions from countries and global community initiatives is possible. Country contributions are made feasible using a modular approach where all countries are able to participate and strategically build their contributions to a global information set over time. The vision we outline will deliver wide-ranging benefits to countries and international efforts to slow the rate of biological invasions and minimize their environmental impacts. These benefits will accrue over time as global coverage and information on alien species increases

The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems

A constant-rate multi-mode system is a hybrid system that can switch freely among a finite set of modes, and whose dynamics is specified by a finite number of real-valued variables with mode-dependent constant rates. Alur, Wojtczak, and Trivedi have shown that reachability problems for constant-rate multi-mode systems for open and convex safety sets can be solved in polynomial time. In this paper, we study the reachability problem for non-convex state spaces and show that this problem is in general undecidable. We recover decidability by making certain assumptions about the safety set. We present a new algorithm to solve this problem and compare its performance with the popular sampling based algorithm rapidly-exploring random tree (RRT) as implemented in the Open Motion Planning Library (OMPL).Comment: 26 page

The minimum energy expenditure shortest path method

This article discusses the addition of an energy parameter to the shortest path execution process; namely, the energy expenditure by a character during execution of the path. Given a simple environment in which a character has the ability to perform actions related to locomotion, such as walking and stair stepping, current techniques execute the shortest path based on the length of the extracted root trajectory. However, actual humans acting in constrained environments do not plan only according to shortest path criterion, they conceptually measure the path that minimizes the amount of energy expenditure. On this basis, it seems that virtual characters should also execute their paths according to the minimization of actual energy expenditure as well. In this article, a simple method that uses a formula for computing vanadium dioxide ($VO_2$) levels, which is a proxy for the energy expenditure by humans during various activities, is presented. The presented solution could be beneficial in any situation requiring a sophisticated perspective of the path-execution process. Moreover, it can be implemented in almost every path-planning method that has the ability to measure stepping actions or other actions of a virtual character

Optimal path planning for nonholonomic robotics systems via parametric optimisation

Abstract. Motivated by the path planning problem for robotic systems this paper considers nonholonomic path planning on the Euclidean group of motions SE(n) which describes a rigid bodies path in n-dimensional Euclidean space. The problem is formulated as a constrained optimal kinematic control problem where the cost function to be minimised is a quadratic function of translational and angular velocity inputs. An application of the Maximum Principle of optimal control leads to a set of Hamiltonian vector field that define the necessary conditions for optimality and consequently the optimal velocity history of the trajectory. It is illustrated that the systems are always integrable when n = 2 and in some cases when n = 3. However, if they are not integrable in the most general form of the cost function they can be rendered integrable by considering special cases. This implies that it is possible to reduce the kinematic system to a class of curves defined analytically. If the optimal motions can be expressed analytically in closed form then the path planning problem is reduced to one of parameter optimisation where the parameters are optimised to match prescribed boundary conditions.This reduction procedure is illustrated for a simple wheeled robot with a sliding constraint and a conventional slender underwater vehicle whose velocity in the lateral directions are constrained due to viscous damping

Localization of correlated sources by array processing using spatial smoothing

In this paper, the classical array processing methods are separated in two classes : uncoupled solutions and global solutions . We expose the method that uses the spatial smooting to decorrelate the received signais . Then we apply these array processing methods to signais that are recorded in an underwater acoustics experiment ; in this situation the spatial smoothing is compulsary . Results are discussed .Dans cet article, nous regroupons les diverses méthodes connues de traitement d'antenne en deux catégories : méthodes découplées, méthodes globales . Nous présentons la méthode du lissage spatial qui permet de décorréler les sources à la réception . Nous appliquons ensuite ces méthodes de traitement d'antenne à des signaux enregistrés au cours d'une expérimentation en acoustique sous-marine dans laquelle une onde monochromatique a été émise dans différentes configurations géométriques et météorologiques . Dans cette situation, le lissage spatial doit être utilisé pour décorréler les trajets multiples

Decomposition-based mission planning for fixed-wing UAVs surveying in wind

This paper presents a new method for planning fixed-wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back-and-forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top-down recursive greedy approach is used to traverse the search space in order to minimise flight time of the survey. This optimisation can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offer greatly reduced survey times in wind. Other improved cost functions have been developed to take into account real world problems, e.g. No Fly Zones, in addition to flight time. A number of real surveys are performed in order to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real-world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multi-rotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of a Unmanned Aerial Vehicle (UAV) aerial survey.</div

Markov dynamic models for long-timescale protein motion

Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements