23 accepted papers
Autonomous navigation often requires the simultaneous optimization of multiple objectives. The most common approach scalarizes these into a single cost function using a weighted sum, but this method is unable to find all possible trade-offs and can therefore miss critical solutions. An alternative, …
We focus on planning minimum-length robot paths to cover environments using the robot's sensor or coverage (e.g. cleaning) tool. Many algorithms use the following framework: (i) compute a grid decomposition of the environment, (ii) partition the grid to be covered by non-overlapping coverage lines (…
Autonomous navigation in ice-covered waters poses significant challenges due to the frequent lack of viable collision-free trajectories. When complete obstacle avoidance is infeasible, it becomes imperative for the navigation strategy to minimize collisions. Additionally, the dynamic nature of ice,
Visibility is a crucial aspect of planning and control of autonomous vehicles (AV), particularly when navigating environments with occlusions. However, when an AV follows a trajectory with multiple occlusions, existing methods evaluate each occlusion individually, calculate a visibility cost for eac
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 objec
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 bo…
Legible motion is intent-expressive, which when employed during social robot navigation, allows others to quickly infer the intended avoidance strategy. Predictable motion matches an observer's expectation which, during navigation, allows others to confidently carryout the interaction. In this work,…
Human supervisors in multi-robot systems are primarily responsible for monitoring robots, but can also be assigned with secondary tasks. These tasks can act as interruptions and can be categorized as either intrinsic, i.e., being directly related to the monitoring task, or extrinsic, i.e., being unr…
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
Vessel transit in ice-covered waters poses unique challenges in safe and efficient motion planning. When the concentration of ice is high, it may not be possible to find collision-free trajectories. Instead, ice can be pushed out of the way if it is small or if contact occurs near the edge of the ic…
In this paper, we study the well-known team orienteering problem where a fleet of robots collects rewards by visiting locations. Usually, the rewards are assumed to be known to the robots; however, in applications such as environmental monitoring or scene reconstruction, the rewards are often subjec
Planning a safe trajectory for an ego vehicle through an environment with occluded regions is a challenging task. Existing methods use some combination of metrics to evaluate a trajectory, either taking a worst case view or allowing for some probabilistic estimate, to eliminate or minimize the risk…
In this letter, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://
Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences. A common framework is to iteratively query the user about which of two presented robot trajectories they prefer. While this minimizes the users effort, a strict choice does not yie…
We study active preference learning as a frame-work for intuitively specifying the behaviour of autonomous robots. A user chooses the preferred behaviour from a set of alternatives, from which the robot learns the user's preferences, modeled as a parameterized cost function. Previous approaches pres…
Enabling a broader range of users to efficiently deploy autonomous mobile robots requires intuitive frameworks for specifying a robot's task and behaviour. We present a novel approach using learning from corrections (LfC), where a user is iteratively presented with a solution to a motion planning pr…
Specifying complex task behaviors while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behavior in a shared environment such as industrial facilities. As non-expert users might have little intuition about how their
In this paper we consider the problem of computing an optimal set of motion primitives for a lattice planner. The objective we consider is to compute a minimal set of motion primitives that t-span a configuration space lattice. A set of motion primitives t-span a lattice if, given a real number t gr…
This paper focuses on the problem of deploying a set of autonomous robots to efficiently monitor multiple types of events in an environment. There is a density function over the environment for each event type representing the weighted likelihood of the event at each location. The robots are heterog…
The ability to navigate uncertain environments from a start to a goal location is a necessity in many applications. While there are many reactive algorithms for online replanning, there has not been much investigation in leveraging past executions of the same navigation task to improve future execut…
In this paper we develop an approach for learning user preferences for complex task specifications through human-robot interaction. We consider the problem of planning robot motion in a known environment, but where a user has specified additional spatial and temporal constraints on allowable robot m…
This paper focuses on the problem of deploying a set of autonomous robots to efficiently service tasks that arrive sequentially in an environment over time. Each task is serviced when the robot visits the corresponding task location. Robots can then redeploy while waiting for the next task to arrive…
The Sat-Tsp language was recently proposed [1] for expressing and solving high-level robotic path planning problems. In this paper we show how different constraints that commonly appear in path planning problems, such as set constraints, counting constraints, and ordering constraints can all be expr…