Adaptive motion planning for a mobile robot

Historically, trapezoidal velocity profiles have been widely used to control engines. Nevertheless, the evolution of robots and their uses has led to the need of using smoother profiles, due to the demand of high precision and delicate movements. It has been shown that this can be achieved by minimizing the change of acceleration and using s-curve profiles. Moreover, to provide a good control of the movement of a robot, it is necessary to ensure that it will meet the desired velocity profile. Therefore, a way to prevent how the wheels will react on the soil becomes highly useful, in order to adapt the supplied torque. This thesis suggests a model to define an appropriate s-curve velocity profile given the desired starting and ending kinematic states for a mobile robot. The study is then focused on a one-wheel system to define the interaction between the soil and a wheel. This interaction is modelled and extended in order to calculate the required torque, drawbar pull and power needed to fulfil the desired s-curve velocity profile. Finally, an introduction to unicycle robots is given as an example of how the proposed models could be applied in the motion planning of a mobile robot. Key words: terramechanics, s-curve, jerk, velocity profileOutgoin

Predictive Whole-Body Control of Humanoid Robot Locomotion

Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the environment or to resort to approximations of it. This thesis investigates predictive and optimal control techniques for tackling humanoid robot motion tasks. They generate control input values from the system model and objectives, often transposed as cost function to minimize. In particular, this thesis tackles several aspects of the humanoid robot locomotion problem in a crescendo of complexity. First, we consider the single step push recovery problem. Namely, we aim at maintaining the upright posture with a single step after a strong external disturbance. Second, we generate and stabilize walking motions. In addition, we adopt predictive techniques to perform more dynamic motions, like large step-ups. The above-mentioned applications make use of different simplifications or assumptions to facilitate the tractability of the corresponding motion tasks. Moreover, they consider first the foot placements and only afterward how to maintain balance. We attempt to remove all these simplifications. We model the robot in contact with the environment explicitly, comparing different methods. In addition, we are able to obtain whole-body walking trajectories automatically by only specifying the desired motion velocity and a moving reference on the ground. We exploit the contacts with the walking surface to achieve these objectives while maintaining the robot balanced. Experiments are performed on real and simulated humanoid robots, like the Atlas and the iCub humanoid robots

Robot motion planning via curve shortening flows

This work will present a series of developments of geometric heat flow method in robot motion planning and estimation. The key of geometric heat flow is to formulate the motion planning problem into a curve shortening problem. By solving the geometric heat flow, an arbitrary initial curve can be deformed to a curve of minimal length, which corresponds to a feasible motion. Preliminary theories and algorithms for motion planning based on geometric heat flow have been developed for driftless control affine systems. The main contribution of this research is to extend the algorithm to robotic systems, which are dynamic systems with drifts and different types of constraint. Early stages of the research focus on adapting the algorithm to solve motion planning problems for systems with drift. To tackle systems with drift, actuated curve length and affine geometric heat flow is proposed. The method is then enriched to solve robot gymnastics motion planning, in which the effect of state constraints is encoded into curve length. Free boundary conditions are also studied to enforce the conservation of the robot's momentum. The second stage of the research focus on the construction of the geometric heat flow framework for robot locomotion planning, which involves hybrid dynamics due to contact. The activation and deactivation of phase-dependent constraints are controlled by activation functions. Lastly, to solve 3D problems in robotics, planning and estimation in SO(3) space is formulated using the geometric heat flow method

Priority-Based Distributed Coordination for Heterogeneous Multi-Robot Systems with Realistic Assumptions

A standing challenge in current intralogistics is to reliably, effectively, yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this article we explore a decentralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and report solutions to prevent or recover from deadlocks in specific situations. We validate the approach empirically in simulation with large, heterogeneous multi-robot fleets (with up to 100 robots) operating in both benchmark and realistic environments

Analysis of Load-Carrying Capacity for Redundant Free-Floating Space Manipulators in Trajectory Tracking Task

The aim of this paper is to analyze load-carrying capacity of redundant free-floating space manipulators (FFSM) in trajectory tracking task. Combined with the analysis of influential factors in load-carrying process, evaluation of maximum load-carrying capacity (MLCC) is described as multiconstrained nonlinear programming problem. An efficient algorithm based on repeated line search within discontinuous feasible region is presented to determine MLCC for a given trajectory of the end-effector and corresponding joint path. Then, considering the influence of MLCC caused by different initial configurations for the starting point of given trajectory, a kind of maximum payload initial configuration planning method is proposed by using PSO algorithm. Simulations are performed for a particular trajectory tracking task of the 7-DOF space manipulator, of which MLCC is evaluated quantitatively. By in-depth research of the simulation results, significant gap between the values of MLCC when using different initial configurations is analyzed, and the discontinuity of allowable load-carrying capacity is illustrated. The proposed analytical method can be taken as theoretical foundation of feasibility analysis, trajectory optimization, and optimal control of trajectory tracking task in on-orbit load-carrying operations

A Method of Energy-Optimal Trajectory Planning for Palletizing Robot

In this work, the energy-optimal trajectory planning and initial pick point searching problem for palletizing robot with high load capacity and high speed are studied, in which the pick point and place point of the robot are fixed to a desired location for each single task. These optimization problems have been transformed to ternary functional extremum problem and parameters optimal selection problem in which the performance index of the problems the rigid-flexible coupling dynamics model of the robot, and the constraint and boundary conditions of the robot are given. The fourth-order Runge-Kutta method, multiple shooting method, and traversing method are used to solve these specific mathematical problems. The effectiveness of the trajectory planning method is validated by the experimental and simulating results; thus the research work done here provides important support for subsequent palletizing robot research

Development and implementation of a B-Spline motion planning framework for autonomous mobile robots

O projeto enquadra-se na área da robótica. A ideia deste projeto é utilizar as propriedades das curvas b-spline para resolver problemas de otimização de motion planning. Esta abordagem permite desviar dos tradicionais motion planning algorithms que são normalmente utilizados. Devido á sua natureza matemática, esta abordagem permite a utilização de teoremas como o Separating Hyperplane Thereoem para realizar o desvio de obstáculos. Um aspecto importante a ter em conta é que este projeto irá ser integrado com os projetos desenvolvidos por outros alunos de modo a participar na competição The Autonomous Ship Challenge, a ser realizada na Noruega.This project fits within the area of robotics. The main idea is to utilize the properties of b-splines curves in order to solve motion planning optimization problems. This approach allows to deviate from the traditional motion planning algorithms, that are usually used. Due to its mathematical nature, this approach allows the use of theorems like the Separating Hyperplane Theorem for the obstacle avoidance problem. An important aspect to notice is that this project will be integrated with the other projects developed by other students in order to participate in "The Autonomous Ship Challenge" competition to be held in Norway