An Algorithm for Task Allocation and Planning for a Heterogeneous Multi-Robot System to Minimize the Last Task Completion Time

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min-max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal-dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min-max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time

Placement and motion planning algorithms for robotic sensing systems

University of Minnesota Ph.D. dissertation. October 2014. Major: Computer Science. Advisor: Prof. Ibrahim Volkan Isler. I computer file (PDF); xxiii, 226 pages.Recent technological advances are making it possible to build teams of sensors and robots that can sense data from hard-to-reach places at unprecedented spatio-temporal scales. Robotic sensing systems hold the potential to revolutionize a diverse collection of applications such as agriculture, environmental monitoring, climate studies, security and surveillance in the near future. In order to make full use of this technology, it is crucial to complement it with efficient algorithms that plan for the sensing in these systems. In this dissertation, we develop new sensor planning algorithms and present prototype robotic sensing systems.In the first part of this dissertation, we study two problems on placing stationary sensors to cover an environment. Our objective is to place the fewest number of sensors required to ensure that every point in the environment is covered. In the first problem, we say a point is covered if it is seen by sensors from all orientations. The environment is represented as a polygon and the sensors are modeled as omnidirectional cameras. Our formulation, which builds on the well-known art gallery problem, is motivated by practical applications such as visual inspection and video-conferencing where seeing objects from all sides is crucial. In the second problem, we study how to deploy bearing sensors in order to localize a target in the environment. The sensors measure noisy bearings towards the target which can be combined to localize the target. The uncertainty in localization is a function of the placement of the sensors relative to the target. For both problems we present (i) lower bounds on the number of sensors required for an optimal algorithm, and (ii) algorithms to place at most a constant times the optimal number of sensors. In the second part of this dissertation, we study motion planning problems for mobile sensors. We start by investigating how to plan the motion of a team of aerial robots tasked with tracking targets that are moving on the ground. We then study various coverage problems that arise in two environmental monitoring applications: using robotic boats to monitor radio-tagged invasive fish in lakes, and using ground and aerial robots for data collection in precision agriculture. We formulate the coverage problems based on constraints observed in practice. We also present the design of prototype robotic systems for these applications. In the final problem, we investigate how to optimize the low-level motion of the robots to minimize their energy consumption and extend the system lifetime.This dissertation makes progress towards building robotic sensing systems along two directions. We present algorithms with strong theoretical performance guarantees, often by proving that our algorithms are optimal or that their costs are at most a constant factor away from the optimal values. We also demonstrate the feasibility and applicability of our results through system implementation and with results from simulations and extensive field experiments

Autonomous Flight, Fault, and Energy Management of the Flying Fish Solar-Powered Seaplane.

The Flying Fish autonomous unmanned seaplane is designed and built for persistent ocean surveillance. Solar energy harvesting and always-on autonomous control and guidance are required to achieve unattended long-term operation. This thesis describes the Flying Fish avionics and software systems that enable the system to plan, self-initiate, and autonomously execute drift-flight cycles necessary to maintain a designated watch circle subject to environmentally influenced drift. We first present the avionics and flight software architecture developed for the unique challenges of an autonomous energy-harvesting seaplane requiring the system to be: waterproof, robust over a variety of sea states, and lightweight for flight. Seaplane kinematics and dynamics are developed based on conventional aircraft and watercraft and upon empirical flight test data. These models serve as the basis for development of flight control and guidance strategies which take the form of a cyclic multi-mode guidance protocol that smoothly transitions between nested gain-scheduled proportional-derivative feedback control laws tuned for the trim conditions of each flight mode. A fault-tolerant airspeed sensing system is developed in response to elevated failure rates arising from pitot probe water ingestion in the test environment. The fault-tolerance strategy utilizes sensor characteristics and signal energy to combine redundant sensor measurements in a weighted voting strategy, handling repeated failures, sensor recovery, non-homogenous sensors, and periods of complete sensing failure. Finally, a graph-based mission planner combines models of global solar energy, local ocean-currents, and wind with flight-verified/derived aircraft models to provide an energy-aware flight planning tool. An NP-hard asymmetric multi-visit traveling salesman planning problem is posed that integrates vehicle performance and environment models using energy as the primary cost metric. A novel A* search heuristic is presented to improve search efficiency relative to uniform cost search. A series of cases studies are conducted with surface and airborne goals for various times of day and for multi-day scenarios. Energy-optimal solutions are identified except in cases where energy harvesting produces multiple comparable-cost plans via negative-cost cycles. The always-on cyclic guidance/control system, airspeed sensor fault management algorithm, and the nested-TSP heuristic for A* are all critical innovation required to solve the posed research challenges.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91453/1/eubankrd_1.pd

PATH PLANNING STRATEGIES FOR VISIBILITY ENHANCEMENT WITH UNMANNED AERIAL VEHICLES IN CLUTTERED ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency