Stochastic search strategies in 2D using agents with limited perception

Puljiz, David; Varga, Maja; Bogdan, Stjepan

doi:10.3182/20120905-3-hr-2030.00161

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Once the target is lost, the interceptor switches to local search mode trying to find the target again. Local search trajectories are generated using Time-Optimal Path Parameterization (TOPP-RA) library (Pham and Pham, 2018) and follow a Levy flight paradigm (Puljiz et al, 2012). The behavior switching is managed by a state machine which implements the proposed Search-Follow-Intercept strategy, with the emphasis of this work being on the Follow and Intercept parts of the strategy, as described in the remainder of the paper.…”

Section: Software Architecturementioning

confidence: 99%

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Barisic¹,

Petric²,

Bogdan³

2022

Self Cite

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This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder’s trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target’s 3D position. Obtained 3D measurements of target’s position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder’s path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV’s motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results.

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Section: Software Architecturementioning

confidence: 99%

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Barisic¹,

Petric²,

Bogdan³

2022

Self Cite

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“…Generating waypoints for navigation planners is an essential aspect of robot team autonomy. In our work, several methods were implemented: (a) waypoint generation using a 2D lawnmover pattern in relatively small areas of known size; (b) Levvy flight 2D waypoint generation for large areas of known size [19]; (c) autonomous 2D [20] and 3D exploration [21] for areas of unknown size. In this work, we briefly discuss our autonomous exploration approach for ERL and MBZIRC competitions, which was later extended to the planner described in [21], based on the exploration tool called 3D-FBET, work of Zhu et al [22].…”

Section: Related Workmentioning

confidence: 99%

From ERL to MBZIRC: Development of An Aerial-Ground Robotic Team for Search and Rescue

Arbanas¹,

Petric²,

Batinović³

et al. 2022

Automation and Control - Theories and Applications

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This chapter describes the efforts of the LARICS team in the 2019 European Robotics League (ERL) Emergency Robots and the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC) robotics competitions. We focus on the implementation of hardware and software modules that enable the deployment of aerial-ground robotic teams in unstructured environments for joint missions. In addition to the overall system specification, we outline the main algorithms for operation in such conditions: autonomous exploration of unknown environments and detection of objects of interest. Analysis of the results shows the success of the developed system in the competition arena of two of the largest outdoor robotics challenges. Throughout the chapter, we highlight the evolution of the robotic system based on the experience gained in the ERL competition. We conclude the chapter with key findings and additional improvement ideas to advance the state of the art in search and rescue applications of heterogeneous robotic teams.

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“…Once the target is lost, the follower switches to local search mode trying to find the target again. Local search trajectories are generated using Time-Optimal Path Parameterization (TOPP-RA) library (Pham and Pham, 2018) and follow a Levy flight paradigm (Puljiz et al, 2012). The behavior switching is managed by a state machine which implements the proposed Search-Follow-Intercept strategy, with the emphasis of this work being on the Follow and Intercept parts of the strategy, as described in the remainder of the paper.…”

Section: Software Architecturementioning

confidence: 99%

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

Barisic,

Petric,

Bogdan

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The work presented in this paper demonstrates our approach to intercepting a faster intruder UAV, inspired by the MBZIRC2020 Challenge 1. By leveraging the knowledge of the shape of the intruder's trajectory we are able to calculate the interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from ZED Mini to extract the 3D position of the target, for which we devise a histogram-of-depth based processing to reduce noise. Obtained 3D measurements of target's position are used to calculate the position, the orientation and the size of a figure-eight shaped trajectory, which we approximate using lemniscate of Bernoulli. Once the approximation is deemed sufficiently precise, measured by Hausdorff distance between measurements and the approximation, an interception point is calculated to position the intercepting UAV right on the path of the target. The proposed method, which has been significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. The results confirmed that an efficient visual perception module which extracts information related to the motion of the target UAV as a basis for the interception, has been developed. The system is able to track and intercept the target which is 30% faster than the interceptor in majority of simulation experiments. Tests in the unstructured environment yielded 9 out of 12 successful results.

show abstract

Stochastic search strategies in 2D using agents with limited perception

Cited by 5 publications

References 17 publications

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder’s Trajectory

From ERL to MBZIRC: Development of An Aerial-Ground Robotic Team for Search and Rescue

Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

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