6 research outputs found
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
In this work, we consider the problem of decentralized multi-robot target
tracking and obstacle avoidance in dynamic environments. Each robot executes a
local motion planning algorithm which is based on model predictive control
(MPC). The planner is designed as a quadratic program, subject to constraints
on robot dynamics and obstacle avoidance. Repulsive potential field functions
are employed to avoid obstacles. The novelty of our approach lies in embedding
these non-linear potential field functions as constraints within a convex
optimization framework. Our method convexifies non-convex constraints and
dependencies, by replacing them as pre-computed external input forces in robot
dynamics. The proposed algorithm additionally incorporates different methods to
avoid field local minima problems associated with using potential field
functions in planning. The motion planner does not enforce predefined
trajectories or any formation geometry on the robots and is a comprehensive
solution for cooperative obstacle avoidance in the context of multi-robot
target tracking. We perform simulation studies in different environmental
scenarios to showcase the convergence and efficacy of the proposed algorithm.
Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M
Active Perception Based Formation Control for Multiple Aerial Vehicles
Autonomous motion capture (mocap) systems for outdoor scenarios involving
flying or mobile cameras rely on i) a robotic front-end to track and follow a
human subject in real-time while he/she performs physical activities, and ii)
an algorithmic back-end that estimates full body human pose and shape from the
saved videos. In this paper we present a novel front-end for our aerial mocap
system that consists of multiple micro aerial vehicles (MAVs) with only
on-board cameras and computation. In previous work, we presented an approach
for cooperative detection and tracking (CDT) of a subject using multiple 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 now
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 as a convex quadratic
objective and non-convex constraints corresponding to angular configurations of
the MAVs w.r.t. the person. We derive it using Gaussian observation model
assumptions within the CDT algorithm. We also show how we embed all the
non-convex constraints, including those for dynamic and static 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 (video link : https://youtu.be/1qWW2zWvRhA). Extensive simulation
results demonstrate the scalability and robustness of our approach. ROS-based
source code is also provided.Comment: 9 pages, 9 Figure
AirCapRL: Autonomous Aerial Human Motion Capture Using Deep Reinforcement Learning
In this letter, we introduce a deep reinforcement learning (RL) based
multi-robot formation controller for the task of autonomous aerial human motion
capture (MoCap). We focus on vision-based MoCap, where the objective is to
estimate the trajectory of body pose and shape of a single moving person using
multiple micro aerial vehicles. State-of-the-art solutions to this problem are
based on classical control methods, which depend on hand-crafted system and
observation models. Such models are difficult to derive and generalize across
different systems. Moreover, the non-linearity and non-convexities of these
models lead to sub-optimal controls. In our work, we formulate this problem as
a sequential decision making task to achieve the vision-based motion capture
objectives, and solve it using a deep neural network-based RL method. We
leverage proximal policy optimization (PPO) to train a stochastic decentralized
control policy for formation control. The neural network is trained in a
parallelized setup in synthetic environments. We performed extensive simulation
experiments to validate our approach. Finally, real-robot experiments
demonstrate that our policies generalize to real world conditions. Video Link:
https://bit.ly/38SJfjo Supplementary: https://bit.ly/3evfo1OComment: Article accepted for publication in Robotics and Automation Letters
(RA-L) and IROS 2020. 8 Pages, 8 figure