Decentralized Hybrid Formation Control of Unmanned Aerial Vehicles

This paper presents a decentralized hybrid supervisory control approach for a team of unmanned helicopters that are involved in a leader-follower formation mission. Using a polar partitioning technique, the motion dynamics of the follower helicopters are abstracted to finite state machines. Then, a discrete supervisor is designed in a modular way for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Furthermore, a formal technique is developed to design the local supervisors decentralizedly, so that the team of helicopters as whole, can cooperatively accomplish a collision-free formation task

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

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

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform

In this paper, a hierarchical multi-UAV simulation platform,called XTDrone, is designed for UAV swarms, which is completely open-source 4 . There are six layers in XTDrone: communication, simulator,low-level control, high-level control, coordination, and human interac-tion layers. XTDrone has three advantages. Firstly, the simulation speedcan be adjusted to match the computer performance, based on the lock-step mode. Thus, the simulations can be conducted on a work stationor on a personal laptop, for different purposes. Secondly, a simplifiedsimulator is also developed which enables quick algorithm designing sothat the approximated behavior of UAV swarms can be observed inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the codes in simulations can be easily transplanted to embeddedsystems. Note that XTDrone can support various types of multi-UAVmissions, and we provide two important demos in this paper: one is aground-station-based multi-UAV cooperative search, and the other is adistributed UAV formation flight, including consensus-based formationcontrol, task assignment, and obstacle avoidance.Comment: 12 pages, 10 figures. And for the, see https://gitee.com/robin_shaun/XTDron