Potential Fields-Aided Motion Planning for Quadcopters in Three-Dimensional Dynamic Environments

Lee, Kyuman; Choi, Daegyun; Kim, Dong-Hoon

doi:10.2514/6.2021-1410

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…To test CA algorithms in more realistic simulation environments, unlike Lee et al [33], one models an urban environment with real datasets. In other words, one processes the following steps to generate static obstacles using a city model [38].…”

Section: Urban Modelingmentioning

confidence: 99%

“…Compared to other relevant articles, the proposed approach enables UAVs to avoid static and moving obstacles in a 3D space along dynamically feasible trajectories without the local minima and GNRON problems. The authors' previous research [33] focused on conceptual validation so that it tested with artificially generated obstacles and did not consider the sensor's specifications when calculating the CA algorithm. However, this study adopts realistic obstacles representing an urban environment for validating practicability.…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021

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Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

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Section: Urban Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021

Self Cite

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“…In recent years, nonlinear controller designs [8][9][10][11] have also been employed to generate safe maneuvers. For safe path planning [12][13][14][15], on the other hand, cost functions are utilized to minimize the flight envelope. Under these approaches, however, conservative maneuvers are generated in comparison with pilot-induced trajectories.…”

Section: Introductionmentioning

confidence: 99%

Safe Motion Planning and Learning for Unmanned Aerial Systems

Perk

Tsourdos

2022

Aerospace

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To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envelope. Inspired by biological systems, in this paper, we introduce a framework that leverages methods in the field of control theory and reinforcement learning to generate feasible, possibly aggressive, trajectories. For the control policies, Dynamic Movement Primitives (DMPs) imitate pilot-induced primitives, and DMPs are combined in parallel to generate trajectories to reach original or different goal points. The stability properties of DMPs and their overall systems are analyzed using contraction theory. For reinforcement learning, Policy Improvement with Path Integrals (PI2) was used for the maneuvers. The results in this paper show that PI2 updated policies are a feasible and parallel combination of different updated primitives transfer the learning in the contraction regions. Our proposed methodology can be used to imitate, reshape, and improve feasible, possibly aggressive, maneuvers. In addition, we can exploit trajectories generated by optimization methods, such as Model Predictive Control (MPC), and a library of maneuvers can be instantly generated. For application, 3-DOF (degrees of freedom) Helicopter and 2D-UAV (unmanned aerial vehicle) models are utilized to demonstrate the main results.

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“…Dealing with motion planning for obstacle avoidance, several methods are available in the literature [6], such as Rapidly-exploring Random Trees (RRT) [7,8], grid-based algorithms [9,10] or Batch Informed Trees (BIT) and Model Predictive Control (MPC) [11]. A very common approach consists in defining artificial potential fields, which drive the robot to the target inside the workspace [12][13][14]. The result of the potential fields is a set of forces, attractive toward the goal and repulsive from the obstacle regions.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

Palmieri

Scoccia

2021

Machines

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This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.

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Potential Fields-Aided Motion Planning for Quadcopters in Three-Dimensional Dynamic Environments

Cited by 6 publications

References 10 publications

Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

Safe Motion Planning and Learning for Unmanned Aerial Systems

Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

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