Sikang Liu

Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping

We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57% compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.

Information-theoretic mapping using Cauchy-Schwarz Quadratic Mutual Information

We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.

High speed navigation for quadrotors with limited onboard sensing

We address the problem of high speed autonomous navigation of quadrotor micro aerial vehicles with limited onboard sensing and computation. In particular, we propose a dual range planning horizon method to safely and quickly navigate quadrotors to specified goal locations in previously unknown and unstructured environments. In each planning epoch, a short-range planner uses a local map to generate a new trajectory. At the same time, a safe stopping policy is found. This allows the robot to come to an emergency halt when necessary. Our algorithm guarantees collision avoidance and demonstrates important advances in real-time planning. First, our novel short range planning method allows us to generate and re-plan trajectories that are dynamically feasible, comply with state and input constraints, and avoid obstacles in real-time. Further, previous planning algorithms abstract away the obstacle detection problem by assuming the instantaneous availability of geometric information about the environment. In contrast, our method addresses the challenge of using the raw sensor data to form a map and navigate in real-time. Finally, in addition to simulation examples, we provide physical experiments that demonstrate the entire algorithmic pipeline from obstacle detection to trajectory execution.

Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments

There is extensive literature on using convex optimization to derive piece-wise polynomial trajectories for controlling differential flat systems with applications to three-dimensional flight for Micro Aerial Vehicles. In this work, we propose a method to formulate trajectory generation as a quadratic program (QP) using the concept of a Safe Flight Corridor (SFC). The SFC is a collection of convex overlapping polyhedra that models free space and provides a connected path from the robot to the goal position. We derive an efficient convex decomposition method that builds the SFC from a piece-wise linear skeleton obtained using a fast graph search technique. The SFC provides a set of linear inequality constraints in the QP allowing real-time motion planning. Because the range and field of view of the robots sensors are limited, we develop a framework of Receding Horizon Planning , which plans trajectories within a finite footprint in the local map, continuously updating the trajectory through a re-planning process. The re-planning process takes between 50 to 300 ms for a large and cluttered map. We show the feasibility of our approach, its completeness and performance, with applications to high-speed flight in both simulated and physical experiments using quadrotors.

Towards Collaborative Mapping and Exploration Using Multiple Micro Aerial Robots

In this paper, we present a system for collaborative mapping and exploration with multiple quad rotor robots. The basic architecture and development of the algorithms for mapping and exploration validate our system with both simulation and real-world experiments. We utilize the 2.5-D structure of typical indoor environments and demonstrate the deployment of multiple autonomous quadrotors equipped with IMUs and laser scanners engaged in collaborative exploration. Estimation, control and planing algorithms are highly integrated in our system to achieve robust and efficient exploration behaviors.

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.