Perception-aware Path Planning

In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for IEEE Transactions on Robotic

Perception-aware time optimal path parameterization for quadrotors

The increasing popularity of quadrotors has given rise to a class of predominantly vision-driven vehicles. This paper addresses the problem of perception-aware time optimal path parametrization for quadrotors. Although many different choices of perceptual modalities are available, the low weight and power budgets of quadrotor systems makes a camera ideal for on-board navigation and estimation algorithms. However, this does come with a set of challenges. The limited field of view of the camera can restrict the visibility of salient regions in the environment, which dictates the necessity to consider perception and planning jointly. The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints. We show in a simulation study that a state-of-the-art controller can track planned trajectories, and we validate the proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202

URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments

A major challenge with off-road autonomous navigation is the lack of maps or road markings that can be used to plan a path for autonomous robots. Classical path planning methods mostly assume a perfectly known environment without accounting for the inherent perception and sensing uncertainty from detecting terrain and obstacles in off-road environments. Recent work in computer vision and deep neural networks has advanced the capability of terrain traversability segmentation from raw images; however, the feasibility of using these noisy segmentation maps for navigation and path planning has not been adequately explored. To address this problem, this research proposes an uncertainty-aware path planning method, URA* using aerial images for autonomous navigation in off-road environments. An ensemble convolutional neural network (CNN) model is first used to perform pixel-level traversability estimation from aerial images of the region of interest. The traversability predictions are represented as a grid of traversal probability values. An uncertainty-aware planner is then applied to compute the best path from a start point to a goal point given these noisy traversal probability estimates. The proposed planner also incorporates replanning techniques to allow rapid replanning during online robot operation. The proposed method is evaluated on the Massachusetts Road Dataset, the DeepGlobe dataset, as well as a dataset of aerial images from off-road proving grounds at Mississippi State University. Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths

Novel path curvature optimization algorithm for intelligent wheelchair to smoothly pass a narrow space

This paper presents a novel algorithm to address the smooth narrow pass traversing issue, which is based on optimizing the curvature of the wheelchair path. Being aware of the fact that the path smoothness is determined by the path curvature and its change rate, after calculating the position of the narrow pass relative to the base frame of the wheelchair from perception sensor data, the algorithm takes the curvature and its change rate of Bezier curve as the optimal objective, and the wheelchair heading and the condition that the Bezier curve polygon should be convex polygon as constraints, and plans a smooth and optimal path for the controlled wheelchair to follow. This process is iterated dynamically to enable the intelligent wheelchair to traverse the narrow pass smoothly. Simulation is firstly conducted to compare the performances of our method and the A*-based path planning navigation algorithm, which shows that the proposed algorithm is able to achieve more smooth path with smaller curvature when the wheelchair traverses narrow path. Furthermore, the algorithm can control the wheelchair to traverse narrow pass smoothly even without any global map and localization. Real experiment with detailed explanation of algorithm implementation is also given to verify the effectiveness of the proposed algorithm