Mobile Robots Control and Path Planning Strategies

Mobile robots gained lots of attention in the last decades. Because of its flexibility and increased capabilities of automation, mobile robots are used in many applications: from domotic, to search and rescue missions, to agriculture, environment protection and many more. The main capability of mobile robots to accomplish a mission is the mobility in the work environment. To move in a certain environment the robots should achieve: guidance, navigation and control. This thesis focuses on guidance and control of mobile robots, with application to certain classes of robots: Vertical Take Off and Landing Unmanned Aerial Vehicles (VTOL UAV) and Differential Wheel robots (DWR). The contribution of this thesis is on modeling and control of the two classes of robots, and on novel strategies of combined control and motion planning for kinodynamic systems. A new approach to model a class of multi-propeller VTOL is proposed, with the aim of generating a general model for a system as a composition of elementary modules such as actuators and payloads. Two control law for VTOL vehicles and DWR are proposed. The goal of the first is to generate a simple yet powerful control to globally asymptotically stabilize a VTOL for acrobatic maneuvers. The second is a simple saturated input control law for trajectory tracking of a DWR model in 2D. About planning, a novel approach to generate non-feasible trajectories for robots that still guarantees a correct path for kinodynamic planning is proposed. The goal is to reduce the runtime of planners to be used in real-time and realistic scenario. Moreover an innovative framework for mobile robots motion planning with the use of Discrete Event Systems theory is introduced. The two proposed approaches allow to build a global, robust, real-time, quasi-optimal, kinodynamic planner suitable for replanning

Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge