A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms amodel predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others'' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25m/s

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

Augmenting user capabilities through an adaptive assistive manipulator

Mención Internacional en el título de doctorAssistive robot manipulators have the potential to increase the independence of disabled persons in activities of daily living. The current designs are mainly limited to pure teleoperation by the user, given the need for keeping the user in the control loop, and the complexity of the tasks and environments in which they operate. This thesis aims to augment the user’s capabilities for performing such tasks by adapting the robot, and its level of assistance, to the user. Methodologies for modeling and benchmarking the complete human-robot system were established, which helped drive the development of different approaches to adaptation. This included a task-oriented optimization of the robot physical structure, approaches for low-level adaptive shared control, and work on interactive learning of, and assistance on completing, simple object manipulation tasks. Three experimental platforms were used: The ASIBOT manipulator of Universidad Carlos III de Madrid (UC3M), the AMOR manipulator of Exact Dynamics, and the iCub humanoid robot.Los manipuladores asistenciales tienen el potencial de incrementar la independencia de personas discapacitadas en sus actividades de la vida diaria. Los diseños actuales se limitan principalmente a una pura teleoperación, pues dada la complejidad de las tareas y del entorno, se necesita mantener al usuario en el lazo de control. Esta tesis pretende mejorar las capacidades del usuario para realizar estas tareas, adaptando el robot y su nivel de asistencia a las necesidades del usuario. Se han establecido metodologías para el modelado y evaluación del comportamiento del sistema formado por humano y robot, lo que ha permitido el desarrollo de diferentes aproximaciones a la adaptación. Esto incluye desde la optimización de la estructura del robot atendiendo a las tareas, la evaluación de diversas aproximaciones al control compartido adaptativo a bajo nivel, al aprendizaje interactivo y el desarrollo de asistencias para completar tareas sencillas de manipulación. Se ha hecho uso de tres plataformas experimentales: el manipulador ASIBOT de la Universidad Carlos III de Madrid (UC3M), el manipulador AMOR de Exact Dynamics y el humanoide iCub.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Alberto Sanfeliú.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Yiannis Demiri

A bending cell for small batches

This article presents the study that is done for the conception of an automated bending cell devoted to the processing of parts in small batches, gathering the real necessities of potential customers. Joining the maximum of possible information, on the present cells, is has been able to conceive an automated bending cell devoted to the bending of parts, with dif-ferent form, weight, thickness, etc., in small batches. To be able to reach the proposed objec-tives, the cell is equipped with auxiliary systems, such as ATC (Automatic Tool Change) allied to a tool warehouse, AGC (Automatic Gripper Change) with three different grippers, a repo-sition station, and a dedicated 7th axis in the press brake designed to dock a standard 6 axes robot, that provide to cell a sufficient grade of autonomy. Allied with the idea of creating a cell for small batches, is introduced the target of getting this cell at lower price as possible. Thus the cell acquires an extended application range very important for potential customers. To get real perception of the money saving when working with this automated bending cell comparisons between Man work times vs. machine work times in production of small batches have been made

Decentralised Monte Carlo Tree Search for Active Perception

We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems

A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one's own strategy to the adversaries is not desirable, this requires each player to independently predict the other players' future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. We also describe a vision-based pipeline that allows each player to estimate its opponent's relative position. We demonstrate that our solution effectively competes against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy

Bridging Action Space Mismatch in Learning from Demonstrations

Learning from demonstrations (LfD) methods guide learning agents to a desired solution using demonstrations from a teacher. While some LfD methods can handle small mismatches in the action spaces of the teacher and student, here we address the case where the teacher demonstrates the task in an action space that can be substantially different from that of the student -- thereby inducing a large action space mismatch. We bridge this gap with a framework, Morphological Adaptation in Imitation Learning (MAIL), that allows training an agent from demonstrations by other agents with significantly different morphologies (from the student or each other). MAIL is able to learn from suboptimal demonstrations, so long as they provide some guidance towards a desired solution. We demonstrate MAIL on challenging household cloth manipulation tasks and introduce a new DRY CLOTH task -- cloth manipulation in 3D task with obstacles. In these tasks, we train a visual control policy for a robot with one end-effector using demonstrations from a simulated agent with two end-effectors. MAIL shows up to 27% improvement over LfD and non-LfD baselines. It is deployed to a real Franka Panda robot, and can handle multiple variations in cloth properties (color, thickness, size, material) and pose (rotation and translation). We further show generalizability to transfers from n-to-m end-effectors, in the context of a simple rearrangement task