Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions, each defined as a sequence of tasks. While executing a task, a robot can either operate autonomously or be teleoperated by the human operator to complete the task at...

Development of a module with driving and walking capability: Study in the feasibility for application with a ZebRo robot

Robots that use legged locomotion have the ability to overcome obstacles and can negotiate a wide range of difficult terrains, such as encountered in outer-space missions. In many practical scenarios however, their applicability is still limited, mainly due to insufficient speed and efficiency. On the other hand, robots that use wheeled...

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous...

A Modeling Tool for Reconfigurable Skills in ROS

Known attempts to build autonomous robots rely on complex control architectures, often implemented with the Robot Operating System platform (ROS). The implementation of adaptable architectures is very often ad hoc, quickly gets cumbersome and expensive. Reusable solutions that support complex, runtime reasoning for robot adaptation have been...

Development of Power System Control for the DeciZebro

The development of swarm robotics strives for resilience, a key factor in this is energy supply. Without energy a robot swarm cannot operate, so a self sustaining power management system is of great importance. In this thesis, the design of the power management system control hardware and firmware for the new DeciZebro is discussed. The power...

Development of a multiple input power conversion system for the DeciZebro

The DeciZebro is a six-legged autonomous robot is being developed by the Zebro team at the TU Delft. The robot should become autonomous and be able to "feed" itself like a real animal. Currently, it is only possible to charge the DeciZebro using a laptop charger. The goal is to allow the robot to charge itself without human intervention using a...

Decentralized Reinforcement Learning of robot behaviors

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific...

Coordination of multiple vessels via distributed nonlinear model predictive control

This work presents a method for multi-robot trajectory planning and coordination based on nonlinear model predictive control (NMPC). In contrast to centralized approaches, we consider the distributed case where each robot has an on-board computation unit to solve a local NMPC problem and can communicate with other robots in its neighborhood. We...

Cooperative collision avoidance for nonholonomic robots

In this paper, we present a method, namely CCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for...

Evaluation of physical damage associated with action selection strategies in reinforcement learning

Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...