Time-Optimal Online Replanning for Agile Quadrotor Flight

Romero, Angel; Penicka, Robert; Scaramuzza, Davide

doi:10.48550/arxiv.2203.09839

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Most recent trajectory generation and tracking methods are proposed either without considering obstacles or considering partially known or simplified obstacles, e.g., gates. For example, in (Romero et al, 2022) they have achieved 60km/h on a defined racing track. This approach uses Model Predictive Contouring Control (MPCC) (Romero et al, 2021) for local replanning at such a speed.…”

Section: Related Workmentioning

confidence: 99%

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Kulathunga

Hamed

Devitt

et al. 2022

IEEE Robot. Autom. Lett.

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This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a profile, external aerodynamic effects that are applied on the quadrotor can be neglected. Hence, we used a simplified motion model to represent the motion of the quadrotor when formulating the Nonlinear Model Predictive Control (NMPC)-based local planner. However, the simplified motion model causes residual dynamics between the high-level planner and the low-level controller. The Sparse Gaussian Process Regression-based technique is proposed to reduce these residual dynamics. The proposed technique is compared with Data-Driven MPC. The comparison results yield that an augmented residual dynamics model-based planner helps to reduce the nominal model error by a factor of 2 on average. Further, we compared the proposed complete framework with four other approaches. The proposed approach outperformed the others in terms of tracking the reference trajectory without colliding with obstacles with less flight time without losing computational efficiency.

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Section: Related Workmentioning

confidence: 99%

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Kulathunga

Hamed

Devitt

et al. 2022

IEEE Robot. Autom. Lett.

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“…The aim of this module is to run in several milliseconds to ensure the safety of quadrotor and keep it close to the global path while simultaneously avoiding unpredicted obstacles. The existing methods for quadrotor online trajectory re-planning can be mostly categorized as polynomials methods, sampling-based methods, and optimization methods [36]. Polynomials and splines are mostly suitable for re-planning since they are computationally efficient.…”

Section: Online Trajectory Re-planningmentioning

confidence: 99%

Guidance, Navigation and Control for Autonomous Quadrotor Flight in an Agricultural Field: The Case of Vineyards

Mokrane

Benallègue

Choukchou-Braham

et al. 2022

Sensors

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In this paper, we present a complete and efficient solution of guidance, navigation and control for a quadrotor platform to accomplish 3D coverage flight missions in mapped vineyard terrains. Firstly, an occupancy grid map of the terrain is used to generate a safe guiding coverage path using an Iterative Structured Orientation planning algorithm. Secondly, way-points are extracted from the generated path and added to them trajectory’s velocities and accelerations constraints. The constrained way-points are fed into a Linear Quadratic Regulator algorithm so as to generate global minimum snap optimal trajectory while satisfying both the pointing and the corridor constraints. Then, when facing unexpected obstacles, the quadrotor tends to re-plan its path in real-time locally using an Improved Artificial Potential Field algorithm. Finally, a geometric trajectory tracking controller is developed on the Special Euclidean group SE(3). The aim of this controller is to track the generated trajectory while pointing towards predetermined direction using the vector measurements provided by the inertial unit. The performance of the proposed method is demonstrated through several simulation results. In particular, safe guiding paths are achieved. Obstacle-free optimal trajectories that satisfy the way-point position, the pointing direction, and the corridor constraints, are successfully generated with optimized platform snap. Besides, the implemented geometric controller can achieve higher trajectory tracking accuracy with an absolute value of the maximum error in the order of 10−3 m.

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“…Measuring coordinates in practice is difficult or at least costly if some specific protocols and/or sensors are used. When drones fly outdoors, we need to consider their adaptability to changes on the fly, in [39] they tackle the problem of flying a quadrotor using time-optimal control policies that can be replanned online when the environment changes or when encountering unknown disturbances. Another reactive approach is based on virtual forces [40].…”

Section: Related Workmentioning

confidence: 99%

Range-Based Reactive Deployment of a Flying Robot for Target Coverage

2022

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Flying robots, also known as drones and unmanned aerial vehicles (UAVs), have found numerous applications in civilian domains thanks to their excellent mobility and reduced cost. In this paper, we focus on a scenario of a flying robot monitoring a set of targets, which are assumed to be moving as a group, to which the sparse distribution of the targets is not applicable. In particular, the problem of finding the optimal position for the flying robot such that all the targets can be monitored by the on-board ground facing camera is considered. The studied problem can be formulated as the conventional smallest circle problem if all the targets’ locations are given. Because it may be difficult to obtain the locations in practice, such as in Global Navigation Satellite Systems (GNSS) dined environments, a range-based navigation algorithm based on the sliding mode control method is proposed. This algorithm navigates the flying robot toward the farthest target dynamically, using the estimated robot–target distances from the received signal strength, until the maximum robot–target distance cannot be further reduced. It is light computation and easily implementable, and both features help to improve the energy efficiency of the flying robot because no heavy computation is required and no special sensing device needs to be installed on the flying robot. The presented solution does not directly solve the smallest circle problem. Instead, our proposed method dynamically navigates the flying robot to the center of the group of targets using the extracted distance information only. Simulations in Matlab and Gazebo have been conducted for both stationery and mobile targets to verify the effectiveness of the proposed approach.

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Time-Optimal Online Replanning for Agile Quadrotor Flight

Cited by 4 publications

References 19 publications

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

Guidance, Navigation and Control for Autonomous Quadrotor Flight in an Agricultural Field: The Case of Vineyards

Range-Based Reactive Deployment of a Flying Robot for Target Coverage

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