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Assistive robots are robots that are designed to help people with disa-bilities in their daily lives. These robots often are either too simple, and thus can-not meet the needs of the user, or are too complex, and thus become overly ex-pensive or unreliable. To mitigate this problem, we propose a system of multi-robot coordination that allows several heterogeneous robots to work coopera-tively to assist an individual. We design care task workflows that cater to the needs of a specific patient, outlining the tasks needed for the user as well as the robots that can complete each task. We then build a ROS simulation to test our coordination methods on two example workflows for PTSD care routines. Our experiments show that our coordination successfully navigates each robot to its task location at the proper time, thus showing the feasibility of these methods for multi-assistive-robot coordination. 

Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT * (Multi-T-RRT * path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented. 

This paper introduces a probabilistic risk assessment (PRA) framework for the path planning to quantify the risk of unmanned aircraft systems’ (UAS) operations to the ground over populated areas. The proposed framework is designed to be flexible enough to address multiple concerns and objectives by utilizing the probabilistic risk exposure map (PREM) of the area of operation and UAS failure mode analysis with corresponding impact probability distributions on the ground. PREM is defined to be the risk of exposure of people or property on the ground to the presence of UAS in the air as a function of position, and itis used to model the distribution of risk exposure over the map. In this study, PREM isconstructed for the impact related risk conditions where their distributions are modeled as a mixture of bivariate normal distributions over the discretized map of the area. Along with PREM, UAS failure modes with ground impact distributions are used in the derivation of the risk function to quantify the risk of being hit by the failing UAS platform for bystanders, properties and the traffic on the ground. Then, utilizing the derived risk function as a planning cost function, the path planner algorithm is used to plan a path that minimizes the risk according to the proposed risk assessment framework. As a pathplanner, optimal bidirectional rapidly-exploring random trees (RRT) is selected due to its fast convergence and optimality guarantee. Finally, the results of simulations for different scenarios are compared and discussed in detail.