Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization

When designing a motion planner for autonomous robots there are usually multiple objectives to be considered. However, a cost function that yields the desired trade-off between objectives is not easily obtainable. A common technique across many applications is to use a weighted sum of relevant objective functions and then carefully adapt the...

Statistically Distinct Plans for Multi-Objective Task Assignment

We study the problem of finding statistically distinct plans for stochastic task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as...

A Unified Approach to Optimally Solving Sensor Scheduling and Sensor Selection Problems in Kalman Filtering

We consider a general form of the sensor scheduling problem for state estimation of linear dynamical systems, which involves selecting sensors that minimize the trace of the Kalman filter error covariance (weighted by a positive semidefinite matrix) subject to polyhedral constraints. This general form captures several well-studied problems...

Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

Many problems in robotics seek to simultaneously optimize several competing objectives under constraints. A conventional approach to solving such multi-objective optimization problems is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto...

Do we use the Right Measure? Challenges in Evaluating Reward Learning Algorithms

Reward learning is a highly active area of research in human-robot interaction (HRI), allowing a broad range of users to specify complex robot behaviour. Experiments with simulated user input play a major role in the development and evaluation of reward learning algorithms due to the availability of a ground truth. In this paper, we review...

Optimizing Task Waiting Times in Dynamic Vehicle Routing

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art...

Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These...

An Improved Greedy Algorithm for Subset Selection in Linear Estimation

In this paper, we consider a subset selection problem in a spatial field where we seek to find a set of k locations whose observations provide the best estimate of the field value at a finite set of prediction locations. The measurements can be taken at any location in the continuous field, and the covariance between the field values at...

Online Multi-Robot Task Assignment with Stochastic Blockages

In this paper we study the multi-robot task assignment problem with tasks that appear online and need to be serviced within a fixed time window in an uncertain environment. For example, when deployed in dynamic, human-centered environments, the team of robots may not have perfect information about the environment. Parts of the environment may...

Informative Path Planning in Random Fields via Mixed Integer Programming

We present a new mixed integer formulation for the discrete informative path planning problem in random fields. The objective is to compute a budget constrained path while collecting measurements whose linear estimate results in minimum error over a finite set of prediction locations. The problem is known to be NP-hard. However, we strive to...

Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at...