Risk-Aware Planning for Sensor Data Collection

With the emergence of low-cost unmanned air vehicles, civilian and military organizations are quickly identifying new applications for affordable, large-scale collectives to support and augment human efforts via sensor data collection. In order to be viable, these collectives must be resilient to the risk and uncertainty of operating in real-world environments. Previous work in multi-agent planning has avoided planning for the loss of agents in environments with risk. In contrast, this dissertation presents a problem formulation that includes the risk of losing agents, the effect of those losses on the mission being executed, and provides anticipatory planning algorithms that consider risk. We conduct a thorough analysis of the effects of risk on path-based planning, motivating new solution methods. We then use hierarchical clustering to generate risk-aware plans for a variable number of agents, outperforming traditional planning methods. Next, we provide a mechanism for distributed negotiation of stable plans, utilizing coalitional game theory to provide cost allocation methods that we prove to be fair and stable. Centralized planning with redundancy is then explored, planning for parallel task completion to mitigate risk and provide further increased expected value. Finally, we explore the role of cost uncertainty as additional source of risk, using bi-objective optimization to generate sets of alternative plans. We demonstrate the capability of our algorithms on randomly generated problem instances, showing an improvement over traditional multi-agent planning methods as high as 500% on very large problem instances

Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk-aware stochastic physical search (RA-SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task-planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location may only be revealed when an agent physically arrives at the location, and the agent may need to use a single resource to both search for and acquire the item of interest. We present exact and heuristic algorithms for solving RA-SPS problems on complete metric graphs. We first formulate the problem as mixed integer linear programming problem. We then develop custom branch and bound algorithms that result in a dramatic reduction in computation time. Using these algorithms, we generate empirical insights into the hardness landscape of the RA-SPS problem and compare the performance of several heuristics

Multi-Agent Sensor Data Collection with Attrition Risk

We introduce a multi-agent route planning problem for col-lecting sensor data in hostile or dangerous environmentswhen communication is unavailable. Solutions must considerthe risk of losing robots as they travel through the environ-ment, maximizing the expected value of a plan. This requiresplans that balance the number of agents used with the riskof losing them and the data they have collected so far. Whilethere are existing approaches that mitigate risk during task as-signment, they do not explicitly account for the loss of robotsas part of the planning process. We analyze the unique prop-erties of the problem and provide a hierarchical agglomera-tive clustering algorithm that finds high value solutions withlow computational overhead. We show that our solution ishighly scalable, exhibiting performance gains on large problem instances with thousands of tasks

Chaotic Motion Planning for Mobile Robots: Progress, Challenges, and Opportunities

Chaotic path planners are a subset of path planning algorithms that use chaotic dynamical systems to generate trajectories throughout an environment. These path planners are imperative in surveillance tasks in the presence of adversarial agents which require the paths to be unpredictable while at the same time guaranteeing complete coverage of the environments. In the online coverage of unknown terrain, the chaotic path planning algorithms can work without the need of the environment map and the designer has additional control over the generated paths relative to other heuristic coverage path planners such as random-walk algorithms. Although chaotic path planners have been studied over the past two decades, there has not been an updated survey on the advances. This paper presents an up-to-date review by providing: an introduction of commonly used chaotic systems and methods for their manipulation; an overview of obstacle avoidance methods used by chaotic path planners; and a discussion on other applications, challenges, and research gaps