Quadrotors are agile platforms. With human experts, they can perform extremely high-speed flights in cluttered environments. However, fully autonomous flight at high speed remains a significant challenge. In this work, we propose a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework. Specifically, we use a series of overlapping spheres to represent the free space of the environment and propose two novel designs that enable the algorithm to plan high-speed quadrotor trajectories in real-time. One is a sampling-based corridor generation method that generates spheres with large overlapped areas (hence overall corridor size) between two neighboring spheres. The second is a Receding Horizon Corridors (RHC) strategy, where part of the previously generated corridor is reused in each replan. Together, these two designs enlarge the corridor spaces in accordance with the quadrotor's current state and hence allow the quadrotor to maneuver at high speeds. We benchmark our algorithm against other state-of-the-art planning methods to show its superiority in simulation. Comprehensive ablation studies are also conducted to show the necessity of the two designs. The proposed method is finally evaluated on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving flight speeds over 13.7 m/s without any prior map of the environment or external localization facility.

Thanks to the rapid evolvement of robotic technologies, robot mowing is emerging to liberate humans from the tedious and time-consuming landscape work. Traditionally, robot mowing is perceived as a "Coverage Path Planning" problem, with a simplification that converts non-convex obstacles into convex obstacles. Besides, the converted obstacles are commonly dilated by the robot's circumcircle for collision avoidance. However when applied to robot mowing, an obstacle in a lawn is usually non-convex, imagine a garden on the lawn, such that the mentioned obstacle processing methods would fill in some concave areas so that they are not accessible to the robot anymore and hence produce inescapable uncut areas along the lawn edge, which dulls the landscape's elegance and provokes rework. To shrink the uncut area around the lawn edge we hereby reframe the problem into a brand new problem, named the "Edge Coverage Path Planning" problem that is dedicated to path planning with the objective to cover the edge. Correspondingly, we propose two planning methods, the "big and small disk" and the "sliding chopstick" planning method to tackle the problem by leveraging image morphological processing and computational geometry skills. By validation, our proposed methods can outperform the traditional "dilation-by-circumcircle" method.

Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories which aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the RRT* planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.

Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values. This paper argues that this does not necessarily lead to a faster search of A* algorithm since its execution relies on relative values instead of absolute ones. As a mitigation, we propose a L* loss, which upper-bounds the number of excessively expanded states inside the A* search. The L* loss, when used in the optimization of state-of-the-art deep neural networks for automated planning in maze domains like Sokoban and maze with teleports, significantly improves the fraction of solved problems, the quality of founded plans, and reduces the number of expanded states to approximately 50%

Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but demands more physical and mental effort. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we study how whole-body feedback affects the performance of different shared-control methods for obstacle avoidance in diverse environments. A Time-Derivative Sigmoid Function (TDSF) is proposed to generate more intuitive force feedback from obstacles. Comprehensive human experiments were conducted, and the results concluded that force feedback enhances the whole-body telelocomotion performance in unfamiliar environments but could reduce performance in familiar environments. Conveying the robot's intention through haptics showed further improvements since the operator can utilize the force feedback for short-distance planning and visual feedback for long-distance planning.

Applications of industrial robotic manipulators such as cobots can require efficient online motion planning in environments that have a combination of static and non-static obstacles. Existing general purpose planning methods often produce poor quality solutions when available computation time is restricted, or fail to produce a solution entirely. We propose a new motion planning framework designed to operate in a user-defined task space, as opposed to the robot's workspace, that intentionally trades off workspace generality for planning and execution time efficiency. Our framework automatically constructs trajectory libraries that are queried online, similar to previous methods that exploit offline computation. Importantly, our method also offers bounded suboptimality guarantees on trajectory length. The key idea is to establish approximate isometries known as $\epsilon$-Gromov-Hausdorff approximations such that points that are close by in task space are also close in configuration space. These bounding relations further imply that trajectories can be smoothly concatenated, which enables our framework to address batch-query scenarios where the objective is to find a minimum length sequence of trajectories that visit an unordered set of goals. We evaluate our framework in simulation with several kinematic configurations, including a manipulator mounted to a mobile base. Results demonstrate that our method achieves feasible real-time performance for practical applications and suggest interesting opportunities for extending its capabilities.

The unmanned aerial vehicles (UAVs) technology, which is moving towards full autonomous flight, requires operation under uncertainties due to dynamic environments, interaction with humans, system faults, and even malicious cyber attacks. Ensuring security and safety is the first step to making the solutions using such systems certifiable and scalable. In this paper, we introduce an autopilot framework called "Multi-level Adaptive Safety Control" (MASC) for the resilient control of autonomous UAVs under large uncertainties and employ it for engine-out automatic landing under severe weather conditions. A. MASC Architecture In 2009, an Airbus A320 passenger plane (US Airways flight 1549) lost both engines minutes after take-off from LaGuardia airport in New York City due to severe bird strikes [1]. Captain Sullenberger safely landed the plane in the nearby Hudson River. Inspired by this story, we aim to equip UAVs with the capability of human pilots to determine if the current mission is still possible after a severe system failure. If not, the mission is re-planned so that it can be accomplished using the remaining capabilities. This is achieved by the proposed autopilot framework, MASC, which is capable of performing safe maneuvers that are traditionally reserved for human pilots.

The government is facing a highly unusual five-day legal challenge to the policy involving at least 10 migrants, campaign groups Care4Calais and Detention Action, and the Public and Commercial Services Union, which represents the vast majority of UK Border Force staff.

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at a faster rate. We show that the problem of creating a teleoperation schedule that minimizes makespan of the system is NP-Hard. We formulate our problem as a Mixed Integer Linear Program, which can be used to optimally solve small to moderate sized problem instances. We also develop an anytime algorithm that makes use of the problem structure to provide a fast and high-quality solution of the operator scheduling problem, even for larger problem instances. Our key insight is to identify blocking tasks in greedily-created schedules and iteratively remove those blocks to improve the quality of the solution. Through numerical simulations, we demonstrate the benefits of the proposed algorithm as an efficient and scalable approach that outperforms other greedy methods.

Abstract--This work introduces a formulation of model predictive control (MPC) which adaptively reasons about the complexity of the model based on the task while maintaining feasibility and stability guarantees. Existing MPC implementations often handle computational complexity by shortening prediction horizons or simplifying models, both of which can result in instability. Inspired by related approaches in behavioral economics, motion planning, and biomechanics, our method solves MPC problems with a simple model for dynamics and constraints over regions of the horizon where such a model is feasible and a complex model where it is not. The approach leverages an interleaving of planning and execution to iteratively identify these regions, which can be safely simplified if they satisfy an exact template/anchor relationship. "[the complex, slow system] is activated when an event is detected that violates the model of the world that [the simple, I. Extending this concept to the field of motion planning yields meta-planning methods which As demand for robotic systems increases in industries change their structure to leverage simple, fast models where like environmental monitoring, industrial inspection, disaster possible and complex, slow ones where the simple model is recovery, and material handling [1-3], so too has the need for inaccurate [5,6]. However, it is not well understood under what motion planning and control algorithms that efficiently handle exact conditions a given dynamical system may leverage a the complexity of their dynamics and constraints.