CONTROL ALGORITHMS FOR GROUPS OF KINEMATIC UNICYCLE AND SKID-STEERING MOBILE ROBOTS WITH RESTRICTED INPUTS

Abstract. The paper presents analytical and practical studies concerning the control problems of a group of Wheeled Mobile Robots (WMRs) subject to physical constraints. Firstly, controllers for achieving trajectory tracking for kinematic unicycle-like and skidsteering mobile robots with restricted control inputs are established. Next, the underlying tracking controllers are applied for group control under the condition of actuator constraints. In particular we are developing control strategies for establishing rigid and convoy-like formations for vehicles with bounded inputs. The group control approach is based on the concepts of virtual robot and virtual formation. The proposed controllers employ smooth bounded functions that can easily be realized. The performance of the resulting controllers are demonstrated by means of numerical and simulation results

A Reactive Path Planning Approach for a Four-wheel Robot by the Decomposition Coordination Method

In this paper, we discuss the problem of safe navi- gation by solving a non-linear model for a four-wheel robot while avoiding the upcoming obstacles that may cross its path using the Decomposition Coordination Method (DC). The method consists of first, choosing a non-linear system with the associated objective functions to optimize. Then we carry on the resolution of the model using the Decomposition Coordination Method,  which allows the non-linearity of the model to be handled locally and ensures coordination through the use of the Lagrange multipliers. An obstacle-avoidance algorithm is presented thus offering a collision-free solution. A numerical application is given to concert the efficiency of the method employed herein along with the simulation results

DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller

Optimal design and experimental verification of a spherical-wheel composite robot with automatic transformation system

This paper presents a design for a dual-mode prototype robot with the advantages of both a spherical robot and wheeled robot. A spherical robot has flexible movement capabilities, and the spherical shell can protect the mechanism and electronic devices. A wheeled mobile robot operates at high speed on a flat road. Its simple structure and control system has made it a popular choice in the field of robotics. Our objective was to develop a new concept robot capable of combining two different locomotion mechanisms to increase the locomotion stability and efficiency. The proposed mobile robot prototype was found to be capable and suitable in different situations. The exchange of modes between the spherical and the wheeled robot was realized by a structural change of the robot. The spherical-wheel mobile robot prototype is composed of a deformable spherical shell system, the propulsion system for the sphere and a wheeled mobile unit module. The exchange of locomotion modes was implemented by changing the geometric structure of spherical shell. The mechanical structure of the composite robot is presented in detail as well as the control system including hardware components and the software. The control system allowed for the automatic transformation of the composite robot between either of the locomotion modes. Based on analysis and simulation, the mechanism was optimized in its configuration and dimension to guarantee that robot had a compact structure and high efficiency. Finally, the experimental results of the transformation and motion processes provided dynamic motion parameters and verified the feasibility of the robot prototype

Optimal design and experimental verification of a spherical-wheel composite robot with automatic transformation system

This paper presents a design for a dual-mode prototype robot with the advantages of both a spherical robot and wheeled robot. A spherical robot has flexible movement capabilities, and the spherical shell can protect the mechanism and electronic devices. A wheeled mobile robot operates at high speed on a flat road. Its simple structure and control system has made it a popular choice in the field of robotics. Our objective was to develop a new concept robot capable of combining two different locomotion mechanisms to increase the locomotion stability and efficiency. The proposed mobile robot prototype was found to be capable and suitable in different situations. The exchange of modes between the spherical and the wheeled robot was realized by a structural change of the robot. The spherical-wheel mobile robot prototype is composed of a deformable spherical shell system, the propulsion system for the sphere and a wheeled mobile unit module. The exchange of locomotion modes was implemented by changing the geometric structure of spherical shell. The mechanical structure of the composite robot is presented in detail as well as the control system including hardware components and the software. The control system allowed for the automatic transformation of the composite robot between either of the locomotion modes. Based on analysis and simulation, the mechanism was optimized in its configuration and dimension to guarantee that robot had a compact structure and high efficiency. Finally, the experimental results of the transformation and motion processes provided dynamic motion parameters and verified the feasibility of the robot prototype

Path Tracking for a Skid-steer Vehicle using Learning-based Model Predictive Control

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2017. 2. 김현진.Skid-steer vehicle can generate a large traction force, which is especially good for navigation on a rough terrain. However, the turning motion is so sensitive to slippage effect that designing a controller is still a challenging problem. Also, the motion of the vehicle is affected not only by wheel motion, but also by the road properties and the characteristics of wheel control. With this in mind, we employ a model predictive control (MPC) with an on-line model learning. The velocity model, which represents the relationship between true vehicle velocity and input command, is learned with an on-line sparse Gaussian process (GP). The on-line sparse GP can reduce the computational complexity of GP and also consistently update the model from the driving data. Finally, combining with MPC makes it possible to generate an optimal policy based on the learned model. Experiments are conducted to test the tracking performance of a skid-steer robot at the indoor and the outdoor environment. The results show the more reliable performance than the method based on a conventional model with parameter adaptation.1 Introduction 1 1.1 Literature review 1 1.2 Thesis contribution 3 1.3 Thesis outline 4 2 On-line sparse Gaussian process for velocity model 5 2.1 Kinematic model 5 2.2 Sparse Gaussian process 7 2.3 On-line updating 10 3 Model predictive control 11 3.1 Iterative linear quadratic regulator 11 3.2 Cost formulation 15 3.3 Summary of the algorithm 16 4 Experiments 18 4.1 Experimental setup 18 4.2 Indoor experimental results 22 4.3 Outdoor experimental results 29 5 Conclusion 32 5.1 Challenges and future works 32 References 34 국문초록 37Maste

Multi-Agent Coordination and Control under Information Asymmetry with Applications to Collective Load Transport

abstract: Coordination and control of Intelligent Agents as a team is considered in this thesis. Intelligent agents learn from experiences, and in times of uncertainty use the knowl- edge acquired to make decisions and accomplish their individual or team objectives. Agent objectives are defined using cost functions designed uniquely for the collective task being performed. Individual agent costs are coupled in such a way that group ob- jective is attained while minimizing individual costs. Information Asymmetry refers to situations where interacting agents have no knowledge or partial knowledge of cost functions of other agents. By virtue of their intelligence, i.e., by learning from past experiences agents learn cost functions of other agents, predict their responses and act adaptively to accomplish the team’s goal. Algorithms that agents use for learning others’ cost functions are called Learn- ing Algorithms, and algorithms agents use for computing actuation (control) which drives them towards their goal and minimize their cost functions are called Control Algorithms. Typically knowledge acquired using learning algorithms is used in con- trol algorithms for computing control signals. Learning and control algorithms are designed in such a way that the multi-agent system as a whole remains stable during learning and later at an equilibrium. An equilibrium is defined as the event/point where cost functions of all agents are optimized simultaneously. Cost functions are designed so that the equilibrium coincides with the goal state multi-agent system as a whole is trying to reach. In collective load transport, two or more agents (robots) carry a load from point A to point B in space. Robots could have different control preferences, for example, different actuation abilities, however, are still required to coordinate and perform load transport. Control preferences for each robot are characterized using a scalar parameter θ i unique to the robot being considered and unknown to other robots. With the aid of state and control input observations, agents learn control preferences of other agents, optimize individual costs and drive the multi-agent system to a goal state. Two learning and Control algorithms are presented. In the first algorithm(LCA- 1), an existing work, each agent optimizes a cost function similar to 1-step receding horizon optimal control problem for control. LCA-1 uses recursive least squares as the learning algorithm and guarantees complete learning in two time steps. LCA-1 is experimentally verified as part of this thesis. A novel learning and control algorithm (LCA-2) is proposed and verified in sim- ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al- gorithm similar to line search methods, and guarantees learning convergence to true values asymptotically. Simulations and hardware implementation show that the LCA-2 is stable for a variety of systems. Load transport is demonstrated using both the algorithms. Ex- periments running algorithm LCA-2 are able to resist disturbances and balance the assumed load better compared to LCA-1.Dissertation/ThesisMasters Thesis Electrical Engineering 201