In this paper, we present STALC, a hierarchical planning approach for multi-robot coordination in real-world environments with significant inter-robot spatial and temporal dependencies. At its core, STALC consists of a multi-robot graph-based planner which combines a topological graph with a novel, computationally efficient mixed-integer programming formulation to generate highly-coupled multi-robot plans in seconds. To enable autonomous planning across different spatial and temporal scales, we construct our graphs so that they capture connectivity between free-space regions and other problem-specific features, such as traversability or risk. We then use receding-horizon planners to achieve local collision avoidance and formation control. To evaluate our approach, we consider a multi-robot reconnaissance scenario where robots must autonomously coordinate to navigate through an environment while minimizing the risk of detection by observers. Through simulation-based experiments, we show that our approach is able to scale to address complex multi-robot planning scenarios. Through hardware experiments, we demonstrate our ability to generate graphs from real-world data and successfully plan across the entire hierarchy to achieve shared objectives.

In this paper, we present an approach for controlling a team of agile fixed-wing aerial vehicles in close proximity to one another. Our approach relies on recedinghorizon nonlinear model predictive control (NMPC) to plan maneuvers across an expanded flight envelope to enable interagent collision avoidance. To facilitate robust collision avoidance and characterize the likelihood of inter-agent collisions, we compute a statistical bound on the probability of the system leaving a tube around the planned nominal trajectory. Finally, we propose a metric for evaluating highly dynamic swarms and use this metric to evaluate our approach. We successfully demonstrated our approach through both simulation and hardware experiments, and to our knowledge, this the first time close-quarters swarming has been achieved with physical aerobatic fixed-wing vehicles.

We report a novel model-based nullspace adaptive trajectory-tracking control (NS-ATTC) algorithm for fully-actuated 6-degree-of-freedom (DOF) underwater vehicles which estimates unknown plant and actuator model parameters simultaneously. We provide a stability and convergence analysis with proof of asymptotically stable tracking error convergence, as well as a preliminary simulation study demonstrating 6-DOF trajectory tracking. The NS-ATTC algorithm does not require acceleration instrumentation and provides a stable online parameter estimate, enabling robust model-based autonomy.

We present a method for providing statistical guarantees on runtime safety and goal reachability for integrated planning and control of a class of systems with unknown nonlinear stochastic underactuated dynamics. Specifically, given a dynamics dataset, our method jointly learns a mean dynamics model, a spatially-varying disturbance bound that captures the effect of noise and model mismatch, and a feedback controller based on contraction theory that stabilizes the learned dynamics. We propose a sampling-based planner that uses the mean dynamics model and simultaneously bounds the closed-loop tracking error via a learned disturbance bound. We employ techniques from Extreme Value Theory (EVT) to estimate, to a specified level of confidence, several constants which characterize the learned components and govern the size of the tracking error bound. This ensures plans are guaranteed to be safely tracked at runtime. We validate that our guarantees translate to empirical safety in simulation on a 10D quadrotor, and in the real world on a physical CrazyFlie quadrotor and Clearpath Jackal robot, whereas baselines that ignore the model error and stochasticity are unsafe.

Planning for multi-robot teams in complex environments is a challenging problem, especially when these teams must coordinate to accomplish a common objective. In general, optimal solutions to these planning problems are computationally intractable, since the decision space grows exponentially with the number of robots. In this paper, we present a novel approach for multi-robot planning on topological graphs using mixed-integer programming. Central to our approach is the notion of a dynamic topological graph, where edge weights vary dynamically based on the locations of the robots in the graph. We construct this graph using the critical features of the planning problem and the relationships between robots; we then leverage mixed-integer programming to minimize a shared cost that depends on the paths of all robots through the graph. To improve computational tractability, we formulated our optimization problem with a fully convex relaxation and designed our decision space around eliminating the exponential dependence on the number of robots. We test our approach on a multi-robot reconnaissance scenario, where robots must coordinate to minimize detectability and maximize safety while gathering information. We demonstrate that our approach is able to scale to a series of representative scenarios and is capable of computing optimal coordinated strategic behaviors for autonomous multi-robot teams in seconds.

Achieving precise, highly-dynamic maneuvers with Unmanned Aerial Vehicles (UAVs) is a major challenge due to the complexity of the associated aerodynamics. In particular, unsteady effectsas might be experienced in post-stall regimes or during sudden vehicle morphing-can have an adverse impact on the performance of modern flight control systems. In this paper, we present a vortex particle model and associated model-based controller capable of reasoning about the unsteady aerodynamics during aggressive maneuvers. We evaluate our approach in hardware on a morphing-wing UAV executing post-stall perching maneuvers. Our results show that the use of the unsteady aerodynamics model improves performance during both fixed-wing and dynamic-wing perching, while the use of wing-morphing planned with quasi-steady aerodynamics results in reduced performance. While the focus of this paper is a pre-computed control policy, we believe that, with sufficient computational resources, our approach could enable online planning in the future.

Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC) [1]. However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap [2], a light-weight point cloud mapping framework, to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban environments. We then demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show that our motion planning algorithm is capable of navigating around a building using a minimalistic set of goal-points. We also show that point cloud history is important for navigating in these types of constrained environments.

Safe and high-speed navigation is a key enabling capability for real world deployment of robotic systems. A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited field of view (FOV) of existing sensor technologies. In this paper, we study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. Further, we extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Our experiments are conducted on a physical robot based on the MIT race car using an RGBD sensor where were able to demonstrate improved performance at 4 m/s compared to a controller not operating on predicted regions of the map.

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.

In this paper, we describe the design and analysis of a fixed-wing unmanned aerial-aquatic vehicle. Inspired by prior work in aerobatic post-stall maneuvers for fixed-wing vehicles [1], we explore the feasibility of executing a water-to-air transition with a fixed-wing vehicle using almost entirely commercial off-the-shelf components (excluding the fuselage). To do this, we first propose a conceptual design based on observations about the dominant forces and dimensionless analysis. We then further refine this concept by building a design tool based on simplified models to explore the design space. To verify the results of the design tool, we use a higher fidelity model along with a direct hybrid trajectory optimization approach to show via numerical simulation that the water-to-air transition is feasible. Finally, we successfully test our design experimentally by hand-piloting a prototype vehicle through the water-to-air transition and discuss our approach for replacing the human-pilot with closed-loop control.

This paper presents an autonomous perching concept for multirotor aerial vehicles. The Autonomous Grasping Robotic Aerial System for Perching (AGRASP)represents a novel integration of robotics perception, vision-based path planning, and biomimetically-inspired manipulation on a small, lightweight aerial robot with highly-constrained sensor and processing capacity. Computationally lightweight perception algorithms pull candidate perch structures out of a complex environment with no a priori knowledge of the operational space. The innovative manipulator design combines both active grasp and passive grip enabling it to maintain hold on the perch even with all power off. We experimentally demonstrate, for the first time, a quadrotor autonomously detecting and landing on a perch relying solely on onboard sensing and processing.

This paper outlines an algorithmic approach for the automatic coordination and planning of heterogeneous multi-robot teams. Specifically, this work addresses the marsupial-based subset of multi-robot teams, where “carrier” robots transport and deploy “passenger” robots. The approach starts with a high-level watershed segmentation of the world to determine the free-space regions accessible by each robot in the team. Topological graph planning then decides the high-level motion plan for each robot between these free-space regions. Finally, a low-level path planner generates optimized, dynamically-feasible trajectories for each robot along the topological path. The performance of the approach is evaluated in simulation and through hardware experiments.

This paper presents a nested marsupial robotic system and its execution of a notional disaster response task. Human supervised autonomy is facilitated by tightly-coupled, high-level user feedback enabling command and control of a bimanual mobile manipulator carrying a quadrotor unmanned aerial vehicle that carries a miniature ground robot. Each robot performs a portion of a mock hazardous chemical spill investigation and sampling task within a shipping container. This work offers an example application for a heterogeneous team of robots that could directly support first responder activities using complementary capabilities of autonomous dexterous manipulation and mobility, autonomous planning and control, and teleoperation. The task was successfully executed during multiple live trials at the DARPA Robotics Challenge Technology Expo in June 2015. A key contribution of the work is the application of a unified algorithmic approach to autonomous planning, control, and estimation supporting vision-based manipulation and non-GPS-based ground and aerial mobility, thus reducing algorithmic complexity across this capability set. The unified algorithmic approach is described along with the robot capabilities, hardware implementations, and human interface, followed by discussion of live demonstration execution and results.

Perching on powerlines to recharge provides a unique opportunity to extend the mission duration capabilities of small-scale UAVs (Unmanned Aerial Vehicles). In this paper, we investigate the feasibility of localizing an aircraft using the magnetic field generated by a current carrying wire through state estimation and hardware development. By using an Extended Kalman Filter to track the real and imaginary components of the magnetic field signal, we overcome the problems posed by the field's phase-amplitude ambiguity and demonstrate the ability to track an aircraft flying at speeds up to 8 m/s at a distance of 4 meters from the wire. We conclude that the achieved performance is adequate for controlling a bird-scale UAV in a dynamic perching maneuver and that our system would generalize to real world scenarios.