Cooperative relative localization for moving UAVs with single link range measurements

Strader, Jared; Gu, Yu; Gross, Jason N.; Petrillo, Matteo De; Hardy, Jeremy

doi:10.1109/plans.2016.7479718

Cited by 27 publications

(23 citation statements)

References 9 publications

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“…This problem is referred to as active localization and is typically addressed in the context of exploration problems as well as Simultaneous Localization and Mapping (SLAM). In some cases, active localization is applied to ensure the observability of system states due to limited sensing, for example, range‐only (Strader, Gu, Gross, De Petrillo, & Hardy, ; Vallicrosa, Ridao, Ribas, & Palomer, ) or bearing‐only (Vander Hook, Tokekar, & Isler, ) scenarios. For example, in Vallicrosa et al (), visual contact is achieved for autonomous docking of an Autonomous Underwater Vehicle (AUV) by ensuring the observability and convergence of the position of a beacon using a Sum of Gaussians (SOG) filter.…”

Section: Related Workmentioning

confidence: 99%

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Strader

Otsu

Agha–mohammadi

2019

Journal of Field Robotics

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Highly accurate real-time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision-based systems to avoid hazards as well as plan safe routes. However, vision-based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision-based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this work, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared to traditional fixed-sensor configurations.

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Section: Related Workmentioning

confidence: 99%

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Strader

Otsu

Agha–mohammadi

2019

Journal of Field Robotics

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“…[12][13][14] Localization for mobile robots in 2D using range only measurements have been demonstrated in literature. [15][16][17][18] In these works, the relative position between robots is estimated after the robots travel through a sequence of positions and orientations where the experimental verifications were not considered therein. Recently, an onboard Bluetooth-based RL method is proposed by Coppola et al 19 for collision avoidance in UAV swarms.…”

Section: Cooperative Rlmentioning

confidence: 99%

Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

Guo

Qiu

Meng

et al. 2017

International Journal of Micro Air Vehicles

118

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This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors' states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

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“…First, if the UAVs are traveling at same speed and direction with parallel trajectories, an infinite number of solutions exist for the relative position of each UAV. This phenomenon is discussed in detail in [7] and could potentially result in divergence of the EKF. This can be avoided by varying the velocity of each UAV and is discussed further in Section IV.…”

Section: B Cooperative Ranging Localizationmentioning

confidence: 99%

Cooperative UAV Navigation using Inter-Vehicle Ranging and Magnetic Anomaly Measurements

Yang

Strader

et al. 2018

2018 AIAA Guidance, Navigation, and Control Conference

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The problem of cooperative localization for a small group of unmanned aerial vehicles (UAVs) in a Global Navigation Satellite System-denied environment is addressed in this paper. The presented approach containstwo sequential steps: first, an algorithm called cooperative ranging localization, formulated as an extended Kalman filter, estimates each UAV's relative pose inside the group using intervehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV's global pose through matching the group's magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 m after flying about 180 km in 1 h. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high-and low-resolution maps as more UAVs are added into the group.

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Cooperative relative localization for moving UAVs with single link range measurements

Cited by 27 publications

References 9 publications

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

Cooperative UAV Navigation using Inter-Vehicle Ranging and Magnetic Anomaly Measurements

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