Multi-robot target detection and tracking: taxonomy and survey

Robin, Cyril; Lacroix, Simon

doi:10.1007/s10514-015-9491-7

Cited by 229 publications

(135 citation statements)

References 120 publications

Supporting

Mentioning

124

Contrasting

Unclassified

Order By: Relevance

“…where ρ e is the exact solution of model (20) and ρ is defined by (28). We note that C δ depends on δ and that lim δ →0 C δ = ∞.…”

Section: B Numerical Solutionmentioning

confidence: 99%

“…Stochasticity in robots' motion and decisions can arise from inherent sensor and actuator noise, especially in small highly resource-restricted platforms. Stochasticity may also be intentionally introduced, for example, when robots are programed to perform random walks for probabilistic search and tracking missions [28], or to switch probabilistically between behavioral states or tasks in a manner similar to social insects. Social insect colonies provide a useful paradigm for robotic swarm control in that they display robust collective behaviors that emerge from the decentralized decisions of numerous individuals, which act on locally perceived information [6].…”

Section: Performance Bounds On Spatial Coveragementioning

confidence: 99%

See 1 more Smart Citation

Performance Bounds on Spatial Coverage Tasks by Stochastic Robotic Swarms

Zhang

Bertozzi

Elamvazhuthi

et al. 2018

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-This paper presents a novel procedure for computing parameters of a robotic swarm that guarantee coverage performance by the swarm within a specified error from a target spatial distribution. The main contribution of this paper is the analysis of the dependence of this error on two key parameters: the number of robots in the swarm and the robot sensing radius. The robots cannot localize or communicate with one another, and they exhibit stochasticity in their motion and task-switching policies. We model the population dynamics of the swarm as an advectiondiffusion-reaction partial differential equation (PDE) with time-dependent advection and reaction terms. We derive rigorous bounds on the discrepancies between the target distribution and the coverage achieved by individual-based and PDE models of the swarm. We use these bounds to select the swarm size that will achieve coverage performance within a given error and the corresponding robot sensing radius that will minimize this error. We also apply the optimal control approach from our prior work in [13] to compute the robots' velocity field and task-switching rates. We validate our procedure through simulations of a scenario, in which a robotic swarm must achieve a specified density of pollination activity over a crop field.

show abstract

“…where ρ e is the exact solution of model (20) and ρ is defined by (28). We note that C δ depends on δ and that lim δ →0 C δ = ∞.…”

Section: B Numerical Solutionmentioning

confidence: 99%

Section: Performance Bounds On Spatial Coveragementioning

confidence: 99%

Performance Bounds on Spatial Coverage Tasks by Stochastic Robotic Swarms

Zhang

Bertozzi

Elamvazhuthi

et al. 2018

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…移動ロボットやビークル自らが周囲環境をセンシングして移動物体を追跡（位置や速度，大きさを推定）するシステムの構築は，様々な分野で必要不可欠な技術である．そのため，車載レーザスキャナやカメラ，ミリ波レーダを用いた移動物体追跡システムが提案され，実用化されている (Cho et al,2014, Fayad and Cherfaoui, 2007, Mertz et al, 2013, Mukhtar et al, 2015, Wang et al, 2015．複数の移動ロボットが近接する場合，通信系を介して各ロボットのセンサ情報を共有することで，移動物体が 1台のロボットの視野外や死角に存在しても他のロボットから観測できるため，移動物体追跡の精度や信頼性が向上する (Ahmad and Lima, 2013, Robin and Lacroix, 2016, Tsokas and Kyriakopoulos, 2012, Wang and Gu, 2012 , Hashimoto et al, 2014, Tamura et al, 2016．近年，マルチレイヤレーザスキャナの普及により，それを利用した環境認識の研究が盛んである (Miyata et al, 2009, Zeng, 2013 …”

Section: 緒言unclassified

Cooperative tracking of moving objects with multiple mobile robots using in-vehicle laser scanner in GNSS-denied environments

Kanaki

Hashimoto

Yoden

et al. 2017

Transactions of the JSME (In Japanese)

View full text Add to dashboard Cite

This paper presents a laser-based tracking (estimation of pose and size) of moving objects using multiple mobile robots in Global-navigation-satellite-system (GNSS)-denied environments. Each robot is equipped with a multilayer laser scanner and detects moving objects, such as people, cars, and bicycles, in its own laser-scanned images by applying an occupancy-gridbased method. It then sends measurement information related to the moving objects to a central server. The central server estimates the objects' poses (positions and velocities) and sizes from the measurement information using Bayesian filter. In this cooperative-tracking method, the nearby robots always share their tracking information, allowing tracking of invisible or partially visible objects. To perform a reliable cooperative tracking, robots have to correctly identify their relative pose. To do so in GNSS-denied environments, the relative pose is estimated by scan matching using laser measurements captured by both sensor nodes. Such cooperative scan matching is performed by 4-points-congruent-sets (4PCS)-matching-based coarse registration and Iterative-closest-point (ICP)-based fine registration methods. The experimental results of tracking a car, a motorcycle, and a pedestrian with two robots in an outdoor GNSS-denied environment validate the proposed method.

show abstract

“…Distributed multi-agent task allocation algorithms have many application domains such as search and rescue missions [11,13,16,18], product manufacturing [5,20], exploration [9], support in healthcare facilities [4], surveillance [1], and target tracking [17]. By removing the need to maintain a connection with a central server, distributed systems facilitate greater mission ranges and robustness to failures compared with centralised methods.…”

Section: Introductionmentioning

confidence: 99%