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

Abstract

Robots in swarms take advantage of localization infrastructure such as a motion capture system or GPS sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure need not be guaranteed, e.g., in GPS denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multi-robot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit inter-robot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve estimation of the tag's location. In particular, we utilize a mixture Monte-Carlo Localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.

Formation control of nonholonomic mobile robots with inaccurate global positions and velocities

“…It is deduced from the Assumption 1 that F j (t) is bounded. Besides, the estimation error f j,2 (t) is also bounded according to (23). Therefore, Δ(t) is definitely bounded, that is, 0 ≤ Δ(t) ≤ 𝜆, where 𝜆 is an upper bound of Δ(t).…”

“…Many methods have been applied to the formation control of multiple robots, such as the behavior‐based method, 10,11 the virtual structure approach, 12,13 the leader‐following scheme, 14‐16 the potential field method, 17 and consensus‐based approach 18,19 . No matter which formation control method is adopted, the position information of robots comes from the local positioning (such as onboard cameras 20‐22 ) or the global positioning (such as ultrawideband (UWB) sensors 23,24 ). It is pointed out that the local position information is precise but not comprehensive whereas the global position information is comprehensive but not accurate.…”

Ad Hoc Mesh Network Localization Using Ultra-Wideband for Mobile Robotics

“…The first approach, followed by [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], used a combination of initial location estimates using the UWB ranging measurements and a regression method followed by the use of a type of Kalman filter to further improve the estimate over time. The second approach followed a Monte Carlo-based simulation similar to particle filters [ 18 ].…”

“…In contrast to the aforementioned methods, the second method for position estimation found in the literature followed a Monte Carlo approach [ 18 ]. The algorithms followed a repeated random sampling process to arrive at a computationally convergent solution or solutions [ 27 , 28 ].…”

A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions

“…However, the approach was only tested in simulation and needs experimentation to validate robustness. Guler et al [12] proposed a different approach based on Monte Carlo localization (MCL) sequential methods for modelling the motion of the tag quadrotor. The quadrotor's motion was restricted to the horizantal plane where it was flying at constant altitude.…”