Flying Fast and Low Among Obstacles

Scherer, Sebastian; Singh, Sanjiv; Chamberlain, Lyle; Saripalli, Srikanth

doi:10.1109/robot.2007.363619

Cited by 82 publications

(41 citation statements)

References 16 publications

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“…Suzuki et al used A* based approach for waypoint generation and RTABU search based optimization approach for trajectory generation [45]. Scherer et al reported an evidence grid with a Laplacian-based potential method for path planning, an obstacle avoidance based on reactive planning, and velocity controller for trajectory generation [47]. In MA, the action space is discretized into action automatons to reduce the search from infinite dimensional space to finite dimensional.…”

Section: Physics-aware Trajectory Planningmentioning

confidence: 99%

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Thakur

Švec

Gupta

2012

Robotics and Autonomous Systems

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This paper describes GPU based algorithms to compute state transition model for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. State transition model is a key component of Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 minutes. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.

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Section: Physics-aware Trajectory Planningmentioning

confidence: 99%

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Thakur

Švec

Gupta

2012

Robotics and Autonomous Systems

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“…Impressive results for obstacle avoidance on a rotorcraft UAV have been shown by Scherer, Singh, Chamberlain, and Saripalli (2007) and Shim, Chung, Kim, and Sastry (2005). In both cases a scanning laser was used, however, on a Yamaha R-MAX rotorcraft UAV, which is able to lift this type of sensor.…”

Section: Related Workmentioning

confidence: 99%

Vision‐based navigation through urban canyons

Hrabar

Sukhatme

2009

Journal of Field Robotics

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We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision-based techniques. Two commonly used vision-based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground-based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T-junctions and 90-deg bends. Experiments on a rotorcraft unmanned aerial vehicle, which was constrained to two-dimensional flight, demonstrate that stereo vision allowed it to detect an obstacle to the front, and optic flow allowed it to turn away from obstacles to the side. We discuss the theory behind these techniques, our experience in implementing them on the robotic platforms, and their suitability to the urban canyon navigation problem. C 2009 Wiley Periodicals, Inc.

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“…3D laser range scanners are expensive and bulky instruments that are not suitable for small drones. A 3D laser scanner with a limited field of view of 30x40 degrees has been used in [13] on an outdoor helicopter with a payload of 29kg. Mini laser range finders have been used by [14], [15], for detection of large obstacles located directly in front of a drone.…”

Section: Introductionmentioning

confidence: 99%

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

Basiri

Schill

Lima³

et al. 2016

IEEE Robot. Autom. Lett.

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Abstract-In a team of autonomous drones, individual knowledge about the relative location of teammates is essential. Existing relative positioning solutions for teams of small drones mostly rely on external systems such as motion tracking cameras or GPS satellites that might not always be accessible. In this letter, we describe an onboard solution to measure the 3-D relative direction between drones using sound as the main source of information. First, we describe a method to measure the directions of other robots from perceiving their engine sounds in the absence of self-engine noise. We then extend the method to use active acoustic signaling to obtain the relative directions in the presence of self-engine noise, to increase the detection range, and to discriminate the identity of robots. Methods are evaluated in real world experiments and a fully autonomous leader-following behavior is illustrated with two drones using the proposed system.

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Flying Fast and Low Among Obstacles

Cited by 82 publications

References 16 publications

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Vision‐based navigation through urban canyons

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

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