Dynamically Feasible Motion Planning for Micro Air Vehicles Using an Egocylinder

Fragoso, Anthony T.; Çığla, Cevahir; Matthies, Larry

doi:10.1007/978-3-319-67361-5_28

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…On the basis of these mentioned capabilities, ICM is very efficient to remove the uncertainty in situations where relevant disturbances, modeling errors and non-linearity are there [5,37]. Standard PID controller was compared with the present ICM controller test results, and it was found from the above discussion, that performance of ICM is far better than that of normal and standard PID controller methods in terms of accuracy and precision with disturbance rejection as swarm robot is affected by non-linearity and uncertainty [17,29,38]. PSO flow chart is given in given in Fig.…”

Section: Experimentation and Simulation Test Results Carried Outmentioning

confidence: 83%

“…The basic method used in [27] depends on the development of three controllers and the composition of an autonomous unmanned vehicle controller. Using technologies that identify offline systems and online parameter identification technologies [10,29], every controller is consequently acclimated to acquire the coveted execution. The objectives of the controllers were, (I) position control drives the robot to the coveted move, pitch and yaw while keeping up a consistent ostensible push in the body outline.…”

Section: Related Workmentioning

confidence: 99%

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A novel algorithm for integrated control model using swarm robots for intruder detection and rescue schedules

et al. 2019

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Due to the development of computer controlled tools and expansion of integrated computing applications, more and more controller functions are turning to software implementations. A novel controlling algorithm is designed for continuous optimization tasks. However, they are used to thoroughly optimize and apply different areas. The most intelligent swarm algorithms have been designed for continuous optimization problems. However, they have been applied to discreet optimization and applications in different areas. This article gives experimental results on the control of swarm robots with the help of integrated control model (ICM), around its own axis. Such methodology is quite impressive in development of applications for surveillance, path planning, intruder and obstacle detection, model errors in communication to remove uncertainty. The ICM control design performance is based on comprehensive swarm robot model for the identification of actuators from testing data. The same ICM controllers are designed to be compared with the PID controllers in a variety of tests and collected feedback found 12.37%, 8.69% and 12.09% improved on the basis of thrust produced in the propellers for surveillance.

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Section: Experimentation and Simulation Test Results Carried Outmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

A novel algorithm for integrated control model using swarm robots for intruder detection and rescue schedules

et al. 2019

View full text Add to dashboard Cite

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“…All the particles in the belief vector are then propagated forward in time by randomly drawing, for each particle, a next state from the distribution defined by T . Each propagated particle is then assigned a weight based on the observation model ( 6), (7). Finally, the particles are drawn at random according to the distribution of weights.…”

Section: Particle Filter Algorithmmentioning

confidence: 99%

“…Fragoso et al [7] proposed a way of keeping only a local egocentric map of the environment in the form of a cylinder that updates in real-time through temporal fusion of limited field-of-view visual information. Some other works [8], [9] and [10] purpose inflation of depth images using C-Space expansion to facilitate path generation inside a disparity space.…”

Section: Introductionmentioning

confidence: 99%

APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance

Ahmad¹,

Sunberg²,

Humbert³

2020

Preprint

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This paper proposes a framework for 3D obstacle avoidance in the presence of partial observability of environment obstacles. The method focuses on the utility of the Artificial Potential Field (APF) controller in a practical setting where noisy and incomplete information about the proximity is inevitable. We propose a Particle Filter (PF) approach to estimate potential obstacle locations in an input depth image stream. The probable candidates are then used to generate an action that maneuvers the robot towards the negative gradient of potential at each time instant. Rigorous experimental validation on a quadrotor UAV demonstrates the robustness and reliability of the method when robot's sensitivity to incorrect perception information can be concerning. The proposed method is highly compute efficient for real-time applications and agile robots.

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“…More recent reactive motion planning algorithms specifically for aerial vehicles include lightweight egospace-based algorithms extended to a quadrotor’s configuration dynamics (Fragoso et al, 2018), or reactively sample safe trajectories in the field of view of the quadrotor, and decouple local obstacle avoidance from global guidance using a global planner (Ryll et al, 2019). Liu et al (2016a) report on an impressive receding horizon -based approach to (local) planning that involves only limited onboard sensing, and which utilizes a local uniform resolution volumetric occupancy grid map and a cost map to find and navigate to safe frontier points (local goals that are closest to the global goal).…”

Section: Introductionmentioning

confidence: 99%

Receding horizon navigation and target tracking for aerial detection of transient radioactivity

Yadav

Sebok

Tanner

2023

The International Journal of Robotics Research

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The paper presents a receding horizon planning and control strategy for quadrotor-type micro aerial vehicle (mav)s to navigate reactively and intercept a moving target in a cluttered unknown and dynamic environment. Leveraging a lightweight short-range sensor that generates a point-cloud within a relatively narrow and short field of view (fov), and an ssd-MobileNet based Deep neural network running on board the mav, the proposed motion planning and control strategy produces safe and dynamically feasible mav trajectories within the sensor fov, which the vehicle uses to autonomously navigate, pursue, and intercept its moving target. This task is completed without reliance on a global planner or prior information about the environment or the moving target. The effectiveness of the reported planner is demonstrated numerically and experimentally in cluttered indoor and outdoor environments featuring maximum speeds of up to 4.5–5 m/s.

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Dynamically Feasible Motion Planning for Micro Air Vehicles Using an Egocylinder

Cited by 8 publications

References 17 publications

A novel algorithm for integrated control model using swarm robots for intruder detection and rescue schedules

A novel algorithm for integrated control model using swarm robots for intruder detection and rescue schedules

APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance

Receding horizon navigation and target tracking for aerial detection of transient radioactivity

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