Infrastructure-free Multi-robot Localization with Ultrawideband Sensors

Abstract

Robots in a swarm take advantage of a motion capture system or GPS sensors to obtain their global position. However, motion capture systems are environment-dependent and GPS sensors are not reliable in occluded environments. For a reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, here we propose an on-board localization framework for multirobot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with an acceptable precision. We validate the effectiveness of our algorithm with simulations and indoor and outdoor experiments on a two-drone setup. The proposed dual MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates a superior performance over the standard particle filter and the extended Kalman Filter.

Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors

“…Toward the goal of freeing the localization framework from environment completely, recently several works have considered an onboard anchor configuration where a moving vehicle equipped with a set of anchors localizes another robot or human [1]- [3], [18], [19]. In [1], a quadrotor equipped with UWB anchors on board tracks a target with a single UWB sensor by employing an iterated EKF.…”

“…Preliminary versions of this work were presented in [3], [19]. The main differences between the current work and [3] are twofold.…”

“…Second, while non-holonomic ground robots were considered in [3], here we develop the results for holonomic vehicles as well. In [19], a dual MCL algorithm was utilized to solve the multi-robot localization objective. Here, we extend the result of [19] with a mixture MCL algorithm and demonstrate a comprehensive set of simulation and experimental results.…”

“…In [19], a dual MCL algorithm was utilized to solve the multi-robot localization objective. Here, we extend the result of [19] with a mixture MCL algorithm and demonstrate a comprehensive set of simulation and experimental results.…”

Cooperative Positioning or Uav Swarms by Fusing Imu/Uwb/GPS With Federal Kalman Filter

“…The method's framework utilizes a dual Monte-Carlo localization (MCL) algorithm. This method outperformed their standard particle filter and extended Kalman filter algorithm [8]. To deal with the contradiction between positioning accuracy and computational complexity, statistical linear regression is used to design the posterior linearization method of measurement model, and the linearized measurement model is used on the algorithm framework of cooperative positioning which is based on belief propagation.…”