Hierarchical Path Planner using Workspace Decomposition and Parallel Task-Space RRTs

This paper presents a hierarchical path planner consisting of two stages: a global planner that uses workspace information to create collision-free paths for the robot end-effector to follow, and multiple local planners running in parallel that verify the paths in the configuration space by expanding a task-space rapidly-exploring random tree (RRT). We demonstrate the practicality of our approach by comparing it with state-of-the-art planners in several challenging path planning problems. While using a single tree, our planner outperforms other single tree approaches in task-space or configuration space (C-space), while its performance and robustness are comparable or better than that of parallelized bidirectional C-space planners

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

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Abstracts 2012: Highlights of Student Research and Creative Endeavors

https://csuepress.columbusstate.edu/abstracts/1004/thumbnail.jp

An integrated study of earth resources in the state of California using remote sensing techniques, volume 2

There are no author-identified significant results in this report

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Review of the Space Applications program, 1974

The purpose of this review is to provide the participants in the National Aeronautics and Space Administration/National Academy of Engineers' Summer Study in Applications a concise overview of the NASA Applications Program as it stands in 1974. The review covers the accomplishments of the various discipline-oriented programs that make up the total Applications Program, discusses the program plan for the 1975 to 1980 period, and examines the anticipated spaceflight capabilities of the 1980's. NASA has requested the National Academy of Engineers to conduct through its Space Applications Board a comprehensive study of the future Space Applications Program encompassing the following: (1) the Applications Program in general, with particular emphasis on practical approaches, including assessment of the socio-economic benefits and (2) how the broad comprehensive program envisioned above influences, or is influenced by, the shuttle system, the principal space transport system of the 1980's