Verified multi-robot planning under uncertainty

Multi-robot systems are being increasingly deployed to solve real-world problems, from warehouses to autonomous fleets for logistics, from hospitals to nuclear power plants and emergency search and rescue scenarios. These systems often need to operate in uncertain environments which can lead to robot failure, uncertain action durations or the inability to complete assigned tasks. In many scenarios, the safety or reliability of these systems is critical to their deployment. Therefore there is a need for robust multi-robot planning solutions that offer guarantees on the performance of the robot team. In this thesis we develop techniques for robust multi-robot task allocation and planning under uncertainty by building on techniques from formal verification. We present three algorithms that solve the problem of task allocation and planning for a multi-robot team operating under uncertainty. These algorithms are able to calculate the expected maximum number of tasks the multi-robot team can achieve, considering the possibility of robot failure. They are also able to reallocate tasks when robots fail. We formalise the problem of task allocation and robust planning for a multi-robot team using Linear Temporal Logic to specify the team's mission and Markov decision processes to model the robots. Our first solution method is a sampling based approach to simultaneous task allocation and planning. Our second solution method separates task allocation and planning for the same problem using auctioning for the former. Our final solution lies midway between the first two using simultaneous task allocation and planning in a sequential team model. We evaluate all solution approaches extensively using a set of tests inspired by existing benchmarks in related fields with a focus on scalability

Probabilistic planning with formal performance guarantees for mobile service robots

We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially