Efficient Simultaneous Task and Motion Planning for Multiple Mobile Robots Using Task Reachability Graphs

In this thesis, we consider the problem of efficient navigation by robots in initially unknown environments while performing tasks at certain locations. In initially unknown environments, the path plans might change dynamically as the robot discovers obstacles along its route. Because robots have limited energy, adaptations to the task schedule of the robot in conjunction with updates to its path plan are required so that the robot can perform its tasks while reducing time and energy expended. However, most existing techniques consider robot path planning and task planning as separate problems. This thesis plans to bridge this gap by developing a unified approach for navigating multiple robots in uncertain environments. We first formalize this as a problem called task ordering with path uncertainty (TOP-U) where robots are provided with a set of task locations to visit in a bounded environment, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The robots must find the order of tasks that reduces the path length to visit the task locations. We then propose an abstraction called a task reachability graph (TRG) that integrates the robots task ordering and path planning. The TRG is updated dynamically based on inter-task path costs calculated by the path planner. A Hidden Markov Model-based technique calculates the belief in the current path costs based on the environment perceived by the robot’s sensors. We then describe a Markov Decision Process-based algorithm used by each robot in a distributed manner to reason about the path lengths between tasks and select the paths that reduce the overall path length to visit the task locations. We have evaluated our algorithm in simulated and hardware robots. Our results show that the TRG-based approach performs up to 60% better in planning and locomotion times with 44% fewer replans, while traveling almost-similar distances as compared to a greedy, nearest task-first selection algorithm

On the origin of fluorine in the Milky Way

The main astrophysical factories of fluorine (19F) are thought to be Type II supernovae, Wolf-Rayet stars, and the asymptotic giant branch (AGB) of intermediate mass stars. We present a model for the chemical evolution of fluorine in the Milky Way using a semi-analytic multi-zone chemical evolution model. For the first time, we demonstrate quantitatively the impact of fluorine nucleosynthesis in Wolf-Rayet and AGB stars. The inclusion of these latter two fluorine production sites provides a possible solution to the long-standing discrepancy between model predictions and the fluorine abundances observed in Milky Way giants. Finally, fluorine is discussed as a possible probe of the role of supernovae and intermediate mass stars in the chemical evolution history of the globular cluster omega Centauri.Comment: 7 pages, 4 figures. MNRAS in pres

Catching Element Formation In The Act

Gamma-ray astronomy explores the most energetic photons in nature to address some of the most pressing puzzles in contemporary astrophysics. It encompasses a wide range of objects and phenomena: stars, supernovae, novae, neutron stars, stellar-mass black holes, nucleosynthesis, the interstellar medium, cosmic rays and relativistic-particle acceleration, and the evolution of galaxies. MeV gamma-rays provide a unique probe of nuclear processes in astronomy, directly measuring radioactive decay, nuclear de-excitation, and positron annihilation. The substantial information carried by gamma-ray photons allows us to see deeper into these objects, the bulk of the power is often emitted at gamma-ray energies, and radioactivity provides a natural physical clock that adds unique information. New science will be driven by time-domain population studies at gamma-ray energies. This science is enabled by next-generation gamma-ray instruments with one to two orders of magnitude better sensitivity, larger sky coverage, and faster cadence than all previous gamma-ray instruments. This transformative capability permits: (a) the accurate identification of the gamma-ray emitting objects and correlations with observations taken at other wavelengths and with other messengers; (b) construction of new gamma-ray maps of the Milky Way and other nearby galaxies where extended regions are distinguished from point sources; and (c) considerable serendipitous science of scarce events -- nearby neutron star mergers, for example. Advances in technology push the performance of new gamma-ray instruments to address a wide set of astrophysical questions.Comment: 14 pages including 3 figure

Multi-Robot Informed Path Planning Under Communication Constraints

Autonomous exploration using multiple robots is an important area of research with applications to extraterrestrial exploration. The use of robots to explore environments can reduce the dangers to human explorers in unstructured environments, such as after a disaster or on extraterrestrial planetary surfaces. In these scenarios, the environment is unknown or only coarsely known beforehand, and there is no pre-existing communications infrastructure to use. We propose an algorithm that balances the goals of communicating collected samples back to a base station, collecting samples from areas of the environment which are less known, and spending as little energy to do this. Our proposed algorithm uses Gaussian Processes to model communications and distribution of information in the environment. Points are selected from the Gaussian Processes and selected for travel for the robot based on a utility function that weights the three goals of the robot

Multi-Robot Informed Path Planning under Communication Constraints

In the unmanned exploration of extraterrestrial surfaces, or when collecting valuable information after disasters, teams of autonomous robots can be deployed to collect and communicate the information to a central server. The environment is often initially unknown and communications between robots are unreliable and intermittent. We propose a unified solution to this problem where each robot uses Gaussian processes (GPs) to model the distribution of information entropy and communication signal strength in the environment. The two GPs are combined into a single objective function representing the utility of different candidate locations to explore and solved as a constrained utility maximization problem. Robots periodically share their collected information and exploration locations with each other to avoid repeated exploration. Initial simulation experiments show that our proposed approach improves on the distance required to reach similar estimations of the phenomena of interest compared to an approach based on information entropy and distance alone

Simultaneous Motion and Task Planning Using Task Reachability Graphs

Task and motion planning are two fundamental problems in robotics which are frequently encountered in many applications for robots. Task planning involves finding a sequence in a set of tasks satisfying some set of constraints. Path planning involves finding a path through the environment which is collision free between a start and goal location. Since, in most cases, a task involves going to a point in the environment to perform some operation, task and path planning are closely related. However, these two problems have been normally addressed as two separate research problems. Only recently researchers have considered combining these two topics using a technique called Simultaneous Task and Motion Planning (STAMP). In this research, I propose a new method to solve the STAMP problem using a framework called a task reachability graph (TRG). A novel feature of this approach is that it incorporates a very practical aspect of robotics - uncertainty in the robot\u27s motion and uncertainty in the environment into the decisions made by the robot to determine the order of performing tasks while traversing the vertices of the TRG. I have validated the proposed algorithm using two Corobot robots performing different numbers of navigation tasks within an indoor environment. I have also compared it with another recent STAMP algorithm called MRTA-RTPP and shown that the TRG-based algorithm performs comparably

Dynamic Robotic Task Allocation using Real Time Path Planning with Field D*

Multi -robot systems comprise of robot teams which operate collectively to perform the tasks that are assigned to them. One of the major problems encountered by multi -robot systems that operate in initially unknown or partially -known environments is that the conditions in the environment can change dynamically as the robots are performing the tasks. This makes it difficult for the robots to ‘plan’ their tasks ahead of time. However, most state -of -the -art task allocation techniques for multi -robot systems do not incorporate a real-time feedback about the situation of the environment encountered by the robots into their task planning. In this research, we propose to address this deficit by developing a two-layered approach - in the bottom layer, robots update their current perception of the environment, while, in the top -layer, they use the current perception to update their plan of performing tasks. We have demonstrated our technique in a scenario where the robots’ tasks correspond to visiting a set of locations and doing an operation at each location. Such scenarios are encountered in various robotics applications such as detecting potential landmines in an automated humanitarian demining scenario or putting out fires in an automated search and rescue scenario, etc. We have implemented our proposed technique on a Corobot robot within our lab environment and shown that the robot can dynamically detect and avoid obstacles and other mobile robots, and re -plan its path to visit its target locations while incorporating changes to its originally planned paths based on the dynamic and static obstacles it discovers

Efficient Simultaneous Motion and Task Planning Using Task Reachability Graphs

We consider the problem where robots are provided with a set of task locations to visit in an environment of known size, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The objective of the robots is to find the order of tasks that reduces the path length (or, energy expended) to visit the task locations in such a scenario. To solve this problem, we propose an abstraction called a task reachability graph (TRG) that integrates the task ordering with the path planning by the robots. The TRG is updated dynamically based on inter-task path costs calculated using a sampling-based path planner, and, a Hidden Markov Model (HMM)-based technique that calculates the belief in the current path costs based on the environment perceived by the robot\u27s sensors. We then describe a Markov Decision Process (MDP)-based algorithm that can be used by each robot in a distributed manner to reason about the path lengths between tasks using the currently available path information, and select the paths that reduce the overall path length to visit the task locations. We have evaluated our algorithm on simulated Corobot robots within different environments while varying the number of task locations, obstacle geometries and number of robots. Our results show that the TRG-based approach performs up to 40% better in terms of distances traveled, 77% fewer replans, 76% less planning and locomotion times, as compared to a greedy, nearest-task-first selection algorithm

Multi-robot informed path planning under communication constraints

Autonomous exploration using multiple robots is a rapidly evolving technology that can reduce the dangers to humans while exploring unstructured environments such as extraterrestrial planetary surfaces, or, building structures following a disaster scene. In both cases, the environment is initially unknown, and likely dangerous for humans to enter and maneuver within. Robots can explore and provide critical information back to the human operators, allowing for more specialized deployments of human teams, reducing the risk those teams are put into. In this work, we propose a preliminary approach to exploration of unknown and unstructured environments using multiple robots in communication constrained environments. In our proposed approach, we plan to use Gaussian Processes to model the dynamics of the information gathered by the robots and the likelihood of communication. Based on these two Gaussian Processes, we will select candidate points and go to the point that best balances the objectives of information gain and communication of information to a base station, with a bias towards communications the longer the robot goes without communicating samples back to the base station. We plan to evaluate this approach in simulation and on physical robots available in the CMANTIC Robotics Lab