Active Perception Based Formation Control for Multiple Aerial Vehicles

Abstract

We present a novel robotic front-end for autonomous aerial motion-capture (mocap) in outdoor environments. In previous work, we presented an approach for cooperative detection and tracking (CDT) of a subject using multiple micro-aerial vehicles (MAVs). However, it did not ensure optimal view-point configurations of the MAVs to minimize the uncertainty in the person's cooperatively tracked 3D position estimate. In this article, we introduce an active approach for CDT. In contrast to cooperatively tracking only the 3D positions of the person, the MAVs can actively compute optimal local motion plans, resulting in optimal view-point configurations, which minimize the uncertainty in the tracked estimate. We achieve this by decoupling the goal of active tracking into a quadratic objective and non-convex constraints corresponding to angular configurations of the MAVs w.r.t. the person. We derive this decoupling using Gaussian observation model assumptions within the CDT algorithm. We preserve convexity in optimization by embedding all the non-convex constraints, including those for dynamic obstacle avoidance, as external control inputs in the MPC dynamics. Multiple real robot experiments and comparisons involving 3 MAVs in several challenging scenarios are presented.

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

“…In a similar research direction, Tallamraju et al described in a recent work a formation control algorithm for active multi-UAV tracking based on MPC [241]. One of the main novelties of this work is that the MPC is built from decoupling the minimization of the tracking error (distance from the UAVs to the person) and the minimization of the formation error (constraints on the relative bearing of the UAVs with respect to the tracked person).…”

“…In more practical terms, the results of [241] enable online calculation of collision-free path planning while tracking a movable subject and maintaining a certain formation configuration around the tracked subject, optimizing the estimation of the object's position during tracking and maintaining it close to the center of the field of view of each of the robots deployed for collaborative tracking. Compared to other recent works, the authors are able to obtain the best accuracy in the estimation of the tracked person's position, while only trading off a negligible increase in error of the selflocalization estimation of each of the tracking robots.…”

Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems

“…The objective of the system of robots is to ensure that the centroid of the system bounding-box x B t reaches a desired destination position x B d t in the vicinity of the target position x T t . Our work is motivated by the application of simultaneous target tracking ( [17], [18]) and payload transportation. The key requirements in our target tracking scenario are, (i) to not lose track of the target, and, (ii) to ensure that the payload and the formation of robots avoid all the obstacles in their vicinity.…”

“…The extension to non-cuboidal payloads is straight-forward as long as its 3-D convex hull vertices are known. The constraint in (18) controls the time evolution of φ t , subject to limits on φ t and ω t . The defined optimization minimizes the weighted sum of squares of φ t and ω t to ensure that payload rolls smoothly while staying within the planned DVBs (see Fig.…”

AirCapRL: Autonomous Aerial Human Motion Capture Using Deep Reinforcement Learning

“…In a laboratory setting MoCap is performed using a large number of precisely calibrated and high-resolution static cameras. To perform human MoCap in an outdoor setting or in an unstructured indoor environment, the use of multiple and autonomous micro aerial vehicles (MAVs) has recently gained attention [1], [2], [3], [4], [5]. Aerial MoCap of humans/animals facilitates several important applications, e.g., search and rescue using aerial vehicles, behavior estimation for endangered animal species, aerial cinematography and sports analysis.…”