Maximizing information gathered within a budget is a relevant problem for information gathering tasks for robots with cost or operating time constraints. This problem is also known as the informative path planning (IPP) problem or correlated orienteering. It can be formalized as that of finding budgeted routes in a graph such that the reward collected by the route is maximized, where the reward at nodes can be dependent. Unfortunately, the problem is NP-Hard and the state of the art methods are too slow to even present an approximate solution online. Here we present Randomized Anytime Orienteering (RAOr) algorithm that provides near optimal solutions while demonstrably converging to an efficient solution in runtimes that allows the solver to be run online. The key idea of our approach is to pose orienteering as a combination of a Constraint Satisfaction Problem and a Traveling Salesman Problem. This formulation allows us to restrict the search space to routes that incur minimum distance to visit a set of selected nodes, and rapidly search this space using random sampling. The paper provides the analysis of asymptotic near-optimality, convergence rates for RAOr algorithms, and present strategies to improve anytime performance of the algorithm. Our experimental results suggest an improvement by an order of magnitude over the state of the art methods in relevant simulation and in real world scenarios.

Agile MAVs are required to operate in cluttered, unstructured environments at high speeds and low altitudes for efficient data gathering. Given the payload constraints and long range sensing requirements, cameras are the preferred sensing modality for MAVs. The computation burden of using cameras for obstacle sensing has forced the state of the art methods to construct world representations on a per frame basis, leading to myopic decision making. In this paper we propose a long range perception and planning approach using cameras. By utilizing FPGA hardware for disparity calculation and image space to represent obstacles, our approach and system design allows for construction of long term world representation whilst accounting for highly non-linear noise models in real time. We demonstrate these obstacle avoidance capabilities on a quadrotor flying through dense foliage at speeds of up to 4 m/s for a total of 1.6 hours of autonomous flights. The presented approach enables high speed navigation at low altitudes for MAVs for terrestrial scouting.

The last decade has seen a massive growth in applications for Micro-Aerial Vehicles (MAVs), due in large part to their versatility for data gathering with cameras, LiDAR and various other sensors. Their ability to quickly go from assessing large spaces from a high vantage points to flying in close to capture high-resolution data makes them invaluable for applications where we are interested in a specific target with an a priori unknown location, e.g. survivors in disaster response scenarios, vehicles in surveillance, animals in wildlife monitoring, etc., a task we will refer to scouting. Our ultimate goal is to enable MAVs to perform autonomous scouting. In this paper, we describe a semantic mapping system designed to support this goal. The system maintains a 2.5D map describing its belief about the location of semantic classes of interest, using forward-looking cameras and state estimation. The map is continuously updated on the fly, using only onboard processing. The system couples a deep learning 2D semantic segmentation algorithm with a novel mapping method to project and aggregate the 2D semantic measurements into a global 2.5D grid map. We train and evaluate our segmentation method on a novel dataset of cars labelled in oblique aerial imagery. We also study the performance of the mapping system in isolation. Finally, we show the integrated system performing a fully autonomous car scouting mission in the field.

Autonomous mobile robots are required to operate in partially known and unstructured environments. It is imperative to guarantee safety of such systems for their successful deployment. Current state of the art does not fully exploit the sensor and dynamic capabilities of a robot. Also, given the non-holonomic systems with non-linear dynamic constraints, it becomes computationally infeasible to find an optimal solution if the full dynamics are to be exploited online. In this paper we present an online algorithm to guarantee the safety of the robot through an emergency maneuver library. The maneuvers in the emergency maneuver library are optimized such that the probability of finding an emergency maneuver that lies in the known obstacle free space is maximized. We prove that the related trajectory set diversity problem is monotonic and sub-modular which enables one to develop an efficient trajectory set generation algorithm with bounded sub-optimality. We generate an off-line computed trajectory set that exploits the full dynamics of the robot and the known obstacle-free region. We test and validate the algorithm on a full-size autonomous helicopter flying up to speeds of 56m/sin partially-known environments. We present results from 4 months of flight testing where the helicopter has been avoiding trees, performing autonomous landing, avoiding mountains while being guaranteed safe.

Capabilities of mobile autonomous systems is often limited by the sensory constraints. Range sensors moving in a fixed pattern are commonly used as sensing modalities on mobile robots. The performance of these sensors can be augmented by actively controlling their configuration for minimizing the expected cost of the mission. The related information gain problem in NP hard. Current methodologies are either computationally too expensive to run online or make simplifying assumptions that fail in complex environments. We present a method to create and learn a policy that maps features calculated online to sensory actions. The policy developed in this work actively controls a nodding lidar to keep the vehicle safe at high velocities and focuses the sensor bandwidth on gaining information relevant for the mission once safety is ensured. It is validated and evaluated on an autonomous full-scale helicopter (Boeing Unmanned Little Bird) equipped with an actively controlled nodding laser. It is able to keep the vehicle safe at its maximum operating velocity, 56 m=s, and reduce the landing zone evaluation time by 500% as compared to passive nodding. The structure of the policy and efficient learning algorithm should generalize to provide a solution for actively controlling a sensor for keeping a mobile robots safe while exploring regions of interest to the robot.

Autonomous systems that navigate in unknown environments encounter a variety of planning problems. The success of any one particular planning strategy depends on the validity of assumptions it leverages about the structure of the problem, e.g., Is the cost map locally convex? Does the feasible state space have good connectivity? We address the problem of determining suitable motion planning strategies that can work on a diverse set of applications. We have developed a planning system that does this by running competing planners in parallel. In this paper, we present an approach that constructs a planner ensemble - a set of complementary planners that lever-age a diverse set of assumptions. Our approach optimizes the submodular selection criteria with a greedy approach and lazy evaluation. We seed our selection with learnt priors on planner performance, thus allowing us to solve new applications without evaluating every planner on that application. We present results in simulation where the selected ensemble outperforms the best single planner and does almost as well as an off-line planner. We also present results from an autonomous helicopter that has flown missions several kilometers long at speeds of up to 56m/s which involved avoiding unmapped mountains, no-fly zones and landing in cluttered areas with trees and buildings. This work opens the door on the more general problem of adaptive motion planning.

Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking a shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in three different environments - field testing results on actual helicopter flights, in simulation with a moving shipdeck for lidar based tracking and in laboratory using an occluded, and, moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.