A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)

This work presents a novel framework for the formation control of multiple autonomous ground vehicles in an on-road environment. Unique challenges of this problem lie in 1) the design of collision avoidance strategies with obstacles and with other vehicles in a highly structured environment, 2) dynamic reconfiguration of the formation to handle different task specifications. In this paper, we design a local MPC-based tracking controller for each individual vehicle to follow a reference trajectory while satisfying various constraints (kinematics and dynamics, collision avoidance, \textit{etc.}). The reference trajectory of a vehicle is computed from its leader's trajectory, based on a pre-defined formation tree. We use logic rules to organize the collision avoidance behaviors of member vehicles. Moreover, we propose a methodology to safely reconfigure the formation on-the-fly. The proposed framework has been validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1

TECHNICAL METHODS OF CLEANING SHIPWRECKS FROM GHOST NETS

Ghost nets are fishing gear lost and left in bodies of water that continue to be fished. Most of the fishing gear that is lost is made of synthetic materials that break down very slowly or not at all in nature and continue to work long after the net is lost. A ghost net drifts in the sea until it catches on an object, most often a shipwreck. This harms both nature and people\u27s economic interests. Currently, the release of shipwrecks and other sunken objects from fragments of lost nets is mainly done by human hands, resp. divers dive to the wreck and use hand tools to free the wreck from fragments of fishing gear. There are innovative robotic systems in the world that can partially replace the work of divers.

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

СИНТЕЗ УПРАВЛЕНИЯ ДЛЯ АВТОНОМНОЙ ГРУППЫ РОБОТОВ С ФАЗОВЫМИ ОГРАНИЧЕНИЯМИ МЕТОДОМ МНОГОСЛОЙНОГО СЕТЕВОГО ОПЕРАТОРА С РАССТАНОВКОЙ ПРИОРИТЕТОВ

We consider a control system synthesis problem for the small group of autonomous robots with state constraints and several possible initial conditions. The main control task for team of robots is to move the robots out of some current position to the specified terminal position without colliding with each other. Typically, the control synthesis for the group of robots consists of two phases: stabilization of the robot with respect to some point of the state space and the design of optimal trajectories. The trajectories must ensure that the robots move from the initial states to certain states of the terminal set without collisions. To avoid collision, the control system uses priorities based, for example, on a distance between the robot and its end position. Since there are phase constraints, ordinary stabilization of robots cannot ensure the safe movement of robots from different initial conditions to the terminal positions. The paper presents our new approach to solving the stabilization problem with phase constraints by multi-layer network operator. We show an example of synthesis of control for the group of four robots.Рассмотрена задача синтеза системы управления для малых групп автономных роботов с фазовыми ограничениями и несколькими возможными начальными условиями. Основная задача управления для группы роботов состоит в перемещении роботов из некоторых текущих позиций в заданные терминальные положения без столкновений между собой. Обычно синтез управления группой роботов состоит из двух этапов: стабилизация роботов относительно некоторой точки пространства состояний; построение оптимальных траекторий. Траектории должны обеспечить движение роботов из начальных состояний в определенные состояния из терминального множества без столкновений. Во избежание столкновений система управления использует приоритеты роботов, основанные, например, на расстоянии между роботом и его конечным положением. Ввиду наличия фазовых ограничений обычная стабилизация роботов не может обеспечить безопасного движения роботов из различных начальных условий в терминальное положение. В работе представлен новый подход авторов к решению задачи стабилизации с фазовыми ограничениями методом многослойного сетевого оператора. В статье приводится пример синтеза управления для четырех роботов

Decentralized Control of Uncertain Multi-Agent Systems with Connectivity Maintenance and Collision Avoidance

This paper addresses the problem of navigation control of a general class of uncertain nonlinear multi-agent systems in a bounded workspace of $\mathbb{R}^n$ with static obstacles. In particular, we propose a decentralized control protocol such that each agent reaches a predefined position at the workspace, while using only local information based on a limited sensing radius. The proposed scheme guarantees that the initially connected agents remain always connected. In addition, by introducing certain distance constraints, we guarantee inter-agent collision avoidance, as well as, collision avoidance with the obstacles and the boundary of the workspace. The proposed controllers employ a class of Decentralized Nonlinear Model Predictive Controllers (DNMPC) under the presence of disturbances and uncertainties. Finally, simulation results verify the validity of the proposed framework.Comment: IEEE European Control Conference (ECC), Limassol, Cyprus, June 201

Formation Control of Robotic Swarm Using Bounded Artificial Forces

Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions