Spatial coverage in routing and path planning problems

Routing and path planning problems that involve spatial coverage have received increasing attention in recent years in different application areas. Spatial coverage refers to the possibility of considering nodes that are not directly served by a vehicle as visited for the purpose of the objective function or constraints. Despite similarities between the underlying problems, solution approaches have been developed in different disciplines independently, leading to different terminologies and solution techniques. This paper proposes a unified view of the approaches: Based on a formal introduction of the concept of spatial coverage in vehicle routing, it presents a classification scheme for core problem features and summarizes problem variants and solution concepts developed in the domains of operations research and robotics. The connections between these related problem classes offer insights into common underlying structures and open possibilities for developing new applications and algorithms

Multi-robot path planning for budgeted active perception with self-organising maps

© 2016 IEEE. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

The Cowl - v.52 -n.8 - Nov 4, 1987

The Cowl - student newspaper of Providence College. Volume 52, Number 8 - November 4, 1987. 20 pages

Probabilistic Maximum Set Cover with Path Constraints for Informative Path Planning

We pose a new formulation for informative path planning problems as a generalisation of the well-known maximum set cover problem. This new formulation adds path constraints and travel costs, as well as a probabilistic observation model, to the maximum set cover problem. Our motivation is informative path planning applications where the observation model can be naturally encoded as overlapping subsets of a set of discrete elements. These elements may include features, landmarks, regions, targets or more abstract quantities, that the robot aims to observe while moving through the environment with a given travel budget. This formulation allows directly modelling the dependencies of observations from different viewpoints. We show this problem is NP-hard and propose a branch and bound tree search algorithm. Simulated experiments empirically evaluate the bounding heuristics, several tree expansion policies and convergence rate towards optimal. The tree pruning allows finding optimal or bounded-approximate solutions in a reasonable amount of time, and therefore indicates our work is suitable for practical applications

Decentralised Monte Carlo Tree Search for Active Perception

We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems

Multi-Goal Path Planning for Spray Writing with Unmanned Aerial Vehicle

Tato práce se zabývá plánováním přes více cílů pro bezpilotní vzdušné prostředky v úloze psaní textu. Motivací je použití bezpilotní helikoptéry k preciznímu sprejování nápisů například na střechy průmyslových budov. Problém psaní textu bezpilotní helikoptérou formulujeme jako plánování přes více cílů a navrhujeme nový font vhodný pro tuto aplikaci. Helikoptéra poté musí při psaní nápisu letět podél zadaného textu s využitím navrhovaného fontu. Problém hledání cesty podél textu lze formulovat jako zobecnění problému obchodního cestujícího, kde trajektorie spojující jednotlivé segmenty písmen musí respektovat dynamická omezení helikoptéry. Na spojení segmentů písmen je použit model Dubinsova vozítka, který umožňuje průlet nalezené trajektorie konstantní rychlostí bez brzdících manévrů. Navržená metoda plánování byla otestována v realistickém simulátoru a experimenty ukazují její použitelnost pro vícerotorovou helikoptéru v úloze psaní textu.This thesis describes the multi-goal path planning method for an Unmanned Aerial Vehicle (UAV) feasible for the spray writing task. The motivation is to use an autonomous UAV for precise spray writing on, e.g., roofs of industrial buildings. We formulate the writing with the UAV as a multi-goal path planning problem, and therefore, a new font suitable for the multi-goal path planning has been designed. In order to perform writing, the UAV has to travel along the input text characters. The problem can be formulated as the generalized traveling salesman problem, in which trajectories between input text segments respect the UAV constraints. We employed the Dubins vehicle to connect input text segments that allow us to traverse the final trajectory on constant speed without sharp and braking maneuvers. The implemented method has been tested in a realistic simulation environment. The experiments showed that the proposed method is feasible for the considered multirotor UAV

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

A Curriculum Guide for the Inclusion of Ecopsychology in an Alternative Education Setting

Ecopsychology is a relatively new field. It is a hybrid of environmentalism and psychology. The study of ecopsychology and nature awareness emphasizes that people, chiefly children, need nature in order to maintain brain health. This study examines the impact that nature has on humans, and focuses on the positive impact nature has on students with ADHD, ADD, autism, learning disabilities, students at-risk, and students at large. The author lobbies for a paradigm shift in traditional curriculum to accept and honor the extensive potential for students\u27 emotional and psychical health. The proposed project focuses on creating approaches to integrate and implement ecopsychology sensitive curriculum into high school alternative education classrooms in Washington State. Adoption of such concepts and practices in alternative school settings are discussed

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering