Optimal, Multi-Modal Control with Applications in Robotics

The objective of this dissertation is to incorporate the concept of optimality to multi-modal control and apply the theoretical results to obtain successful navigation strategies for autonomous mobile robots. The main idea in multi-modal control is to breakup a complex control task into simpler tasks. In particular, number of control modes are constructed, each with respect to a particular task, and these modes are combined according to some supervisory control logic in order to complete the overall control task. This way of modularizing the control task lends itself particularly well to the control of autonomous mobile robot, as evidenced by the success of behavior-based robotics. Many challenging and interesting research issues arise when employing multi-modal control. This thesis aims to address these issues within an optimal control framework. In particular, the contributions of this dissertation are as follows: We first addressed the problem of inferring global behaviors from a collection of local rules (i.e., feedback control laws). Next, we addressed the issue of adaptively varying the multi-modal control system to further improve performance. Inspired by adaptive multi-modal control, we presented a constructivist framework for the learning from example problem. This framework was applied to the DARPA sponsored Learning Applied to Ground Robots (LAGR) project. Next, we addressed the optimal control of multi-modal systems with infinite dimensional constraints. These constraints are formulated as multi-modal, multi-dimensional (M3D) systems, where the dimensions of the state and control spaces change between modes to account for the constraints, to ease the computational burdens associated with traditional methods. Finally, we used multi-modal control strategies to develop effective navigation strategies for autonomous mobile robots. The theoretical results presented in this thesis are verified by conducting simulated experiments using Matlab and actual experiments using the Magellan Pro robot platform and the LAGR robot. In closing, the main strength of multi-modal control lies in breaking up complex control task into simpler tasks. This divide-and-conquer approach helps modularize the control system. This has the same effect on complex control systems that object-oriented programming has for large-scale computer programs, namely it allows greater simplicity, flexibility, and adaptability.Ph.D.Committee Chair: Egerstedt, Magnus; Committee Member: Ferri, Bonnie; Committee Member: Lee, Chin-Hui; Committee Member: Reveliotis, Spyros; Committee Member: Yezzi, Anthon

Multi-bot Easy Control Hierarchy

The goal of our project is to create a software architecture that makes it possible to easily control a multi-robot system, as well as seamlessly change control modes during operation. The different control schemes first include the ability to implement on-board and off-board controllers. Second, the commands can specify either actuator level, vehicle level, or fleet level behavior. Finally, motion can be specified by giving a waypoint and time constraint, a velocity and heading, or a throttle and angle. Our code is abstracted so that any type of robot - ranging from ones that use a differential drive set up, to three-wheeled holonomic platforms, to quadcopters - can be added to the system by simply writing drivers that interface with the hardware used and by implementing math packages that do the required calculations. Our team has successfully demonstrated piloting a single robots while switching between waypoint navigation and a joystick controller. In addition, we have demonstrated the synchronized control of two robots using joystick control. Future work includes implementing a more robust cluster control, including off-board functionality, and incorporating our architecture into different types of robots

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

MPC for Robot Manipulators with Integral Sliding Modes Generation

This paper deals with the design of a robust hierarchical multiloop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system and the combination of a model predictive control (MPC) module with an integral sliding mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodeled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfiling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the MPC optimization problem relying on a set of linearized decoupled single-input-single-output (SISO) systems that are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data

Autonomous Locomotion Mode Transition Simulation of a Track-legged Quadruped Robot Step Negotiation

Multi-modal locomotion (e.g. terrestrial, aerial, and aquatic) is gaining increasing interest in robotics research as it improves the robots environmental adaptability, locomotion versatility, and operational flexibility. Within the terrestrial multiple locomotion robots, the advantage of hybrid robots stems from their multiple (two or more) locomotion modes, among which robots can select from depending on the encountering terrain conditions. However, there are many challenges in improving the autonomy of the locomotion mode transition between their multiple locomotion modes. This work proposed a method to realize an autonomous locomotion mode transition of a track-legged quadruped robot steps negotiation. The autonomy of the decision-making process was realized by the proposed criterion to comparing energy performances of the rolling and walking locomotion modes. Two climbing gaits were proposed to achieve smooth steps negotiation behaviours for energy evaluation purposes. Simulations showed autonomous locomotion mode transitions were realized for negotiations of steps with different height. The proposed method is generic enough to be utilized to other hybrid robots after some pre-studies of their locomotion energy performances

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl