Mobile Robots: An Introduction Amirkabir University of Technology Computer Engineering & Information Technology Department http://ce.aut.ac.ir/~shiry/lecture/robotics-2004/robotics04.html
Introduction Mobile robots or vehicles usually use wheels (smooth terrains) or legs (rough terrains) attached to a base Few mobile robots have manipulators, because they need to be small, strong, reliable and cheap hard to achieve Another problem is power: either umbilical cord restrict motion; Or batteries run out and also need wireless connection The biggest problem with robots is that usually they need a structured environment to function
Wheeled Vehicles Most mobile machines roll on wheels, because: wheels are simpler to control, pose fewer stability problems, use less energy, can go faster and are reasonably maneuverable However, wheels are generally good only for smooth solid terrain. On soft ground they slip and get bogged down Also to overcome obstacles wheels need to be bigger than the obstacle
Wheeled Mobile Robots (WMR)
Architecture of Robotic Systems Environmental sensors Motion planner Controller Mechanical Structure Mechanical Structure Kinematics model Dynamics model Configuration sensor Actuators: Electrical, Hydraulic, Pneumatic, Artificial Muscle Computation and controllers Sensors Communications User interface Power conversion unit
Locomotion Locomotion: how the robot moves through its environment Drive types, kinematics, velocity control, omni-directional drive, steering, car like robot,
Dead Reckoning (odometery) Odometry is the most widely used method for determining the momentary position of a mobile robot. Left and right wheel encoders measure incremental traveled distance. It provides easily accessible real-time positioning information in-between periodic absolute position measurements. Odometery Error?
Robot Control Architecture Robot architecture provides a set of principles for organizing control systems. Four basic types: Deliberate / planner based. Purely reactive. Behavior based. Hybrid.
Deliberate Approach Traditional, top-down planner-based / deliberative strategies. Rely on centralized world for verifying sensory information and generating actions in the world. Information in the world is used by the planner to produce the most appropriate actions for the agent.
Deliberate Approach
Purely Reactive An approach to achieve real-time performance in autonomous agents. Bottom up approach. Agent s control strategy is embedded into a collection of preprogrammed action pairs. Maintain no internal models and perform no searches. Simple functional mapping between stimuli and appropriate responses. Mappings rely on a direct relationship between sensing and action and fast feedback from environment.
Purely Reactive Effective for problems completely specified at design time. Cannot store information dynamically and this strategy is therefore inflexible at run time. Amount of computation performed at runtime demonstrates the division between reactive and deliberate strategies.
Behavior-based Extension of reactive architectures. Falls between reactive and plannerbased extremes.
Behavior-based Behavior based have some of the properties of reactive systems and contain reactive components, but the computation is not limited to simple functional mapping. Can store various forms of state and implement various forms of representation. There is much freedom of interpretation as to what a behavior-based system actually is, which has therefore promoted much research in the field.
Behavior-based General definition of behavior based architecture. Does not employ centralized representations operated by a reasoning engine. Relies on forms of distributed representations and performs distributed computations on them. Behaviors are typically more time-extended than actions of reactive systems.
Behavior-based Constraints on Behavior Based Systems Behaviors must be relatively simple Incrementally added to the system. Execution not be serialized Must be more time-extended than simple atomic actions of the particular agent. Must interact with other behaviors through the world rather than internally through the system.
Brooks subsumption architecture Is a hierarchy of task-accomplishing behaviors Each behavior is a rather simple rule-like structure Each behavior competes with others to exercise control over the agent Lower layers represent more primitive kinds of behavior (such as avoiding obstacles), and have precedence over layers further up the hierarchy The resulting systems are, in terms of the amount of computation they do, extremely simple
Brooks subsumption architecture
Hybrid Compromise between reactive and deliberate approaches. Usually has a reactive system for low level control and a planner for higherlevel decision making.
Hybrid Separated into two or more communicating but otherwise independent parts. Low level reactive process: immediate safety of the agent. Higher level: uses planner to select action sequences.
Hybrid Examples of hybrid systems. Reactive planning in reactive action packages (raps). Procedural reasoning systems. Internalized plans. Contingency plans.
Mobile Robot System Overview high-level Abstraction level Motion Planning: Given a known world and a cooperative mechanism, how do I get there from here? Localization: Given sensors and a map, where am I? Vision: If my sensors are eyes, what do I do? Mapping: Given sensors, how do I create a useful map? Bug Algorithms: Given an unknowable world but a known goal and local sensing, how can I get there from here? Kinematics: if I move this motor somehow, what happens in other coordinate systems? Control (PID): what voltage should I set over time? low-level Motor Modeling: what voltage should I set now?
Motion Planning Find a path connecting an initial configuration to goal configuration without collision with obstacles Motion Planning Methods Roadmap Approaches Cell Decomposition Potential Fields Bug Algorithms
Motion Planning Methods The motion planning problem consists of the following: Input geometric descriptions of a robot and its environment (obstacles) initial and goal configurations Output a path from start to finish (or the recognition that none exists) q goal Applications Robot-assisted surgery Automated assembly plans Drug-docking and analysis Moving pianos around... q robot
Motion Planning Methods (1) Roadmap approaches (2) Cell decomposition Goal reduce the N-dimensional configuration space to a set of one-d paths to search. Goal account for all of the free space (3) Potential Fields (4) Bug algorithms Goal Create local control strategies that will be more flexible than those above Limited knowledge path planning
Roadmap: Visibility Graphs Visibility graphs: In a polygonal (or polyhedral) configuration space, construct all of the line segments that connect vertices to one another (and that do not intersect the obstacles themselves). Formed by connecting all visible vertices, the start point and the end point, to each other. For two points to be visible no obstacle can exist between them Paths exist on the perimeter of obstacles From Cfree, a graph is defined Converts the problem into graph search. Dijkstra s algorithm O(N^2) N = the number of vertices in C-space
The Visibility Graph goal start Since the map was in C-space, each line potentially represents part of a path from the start to the goal.
Roadmap: Voronoi diagrams GVG is formed by paths equidistant from the two closest objects maximizing the clearance between the obstacles. This generates a very safe roadmap which avoids obstacles as much as possible
Exact Cell Decomposition The robot free space (Cfree) is decomposed into simple regions (cells) Decomposition of the free space into trapezoidal & triangular cells Connectivity graph representing the adjacency relation between the cells Transform the sequence of cells into a free path (e.g., connecting the mid-points of the intersection of two consecutive cells)
Potential Field Method The goal location generates an attractive potential pulling the robot towards the goal The obstacles generate a repulsive potential pushing the robot far away from the obstacles The negative gradient of the total potential is treated as an artificial force applied to the robot -- Let the sum of the forces control the robot C-obstacles
Bug Algorithms Path planning with limited knowledge - Insect-inspired bug algorithms known direction to goal Goal otherwise only local sensing (walls/obstacles encoders) reasonable world 1) finite obstacles in any finite range 2) a line will intersect an obstacle Start finite times
Mobile Robot Mapping Answering robotics big questions How to get a map of an environment with imperfect sensors (Mapping) How a robot can tell where it is on a map (localization) Getting from place to place under uncertain conditions Using vision effectively
Localization via mapping Map the area around the robot Match the result to the known map of the world
Sonar sensing The sponge sonar timeline 0.5s 75µs the transducer goes into a chirp is emitted into the environment typically when reverberations from the initial chirp have stopped receiving mode and awaits a signal... after a short time, the signal will be too weak to be detected Sonar (sound navigation and ranging): range sensing using acoustic (i.e., sound) signal Blanking time Attenuation Polaroid sonar emitter/receivers Why is sonar sensing limited to between ~12 in. and ~25 feet?
Sonar modeling initial time response accumulated responses blanking time cone width spatial response Sonar amplitude profile: 3-D cone with side lobes
Laser Range Finder Laser range finders determine distance by measuring either the time it takes for a laser beam to be reflected back to the robot or by measuring where the laser hits the object.
Adaptation and Learning In Autonomous Robots Learning to interpret sensor information Recognizing objects in the environment is difficult Sensors provide prohibitively large amounts of data Programming of all required objects is generally not possible Learning new strategies and tasks New tasks have to be learned on-line in the home Different inhabitants require new strategies even for existing tasks Adaptation of existing control policies User preferences can change dynamically Changes in the environment have to be reflected
Learning Approaches for Robot Systems Supervised learning by teaching Robots can learn from direct feedback from the user that indicates the correct strategy The robot learns the exact strategy provided by the user Learning from demonstration (Imitation) Robots learn by observing a human or a robot perform the required task The robot has to be able to understand what it observes and map it onto its own capabilities Learning by exploration Robots can learn autonomously by trying different actions and observing their results The robot learns a strategy that optimizes reward
Reinforcement Learning for Robot Navigation Learning from rewards and punishments in the environment Give reward for reaching goal Give punishment for getting lost Give punishment for collisions
Course Presentation The preference is on topics related to autonomous mobile robots Topics: Localization Motion planning Navigation Mapping Architecture Control Learning Sensing Robcup
Presentation The aim is to provide a comprehensive background in the fundamentals of Autonomous mobile robots. Each presentation is organized as a teaching practice.
Presentation For each presentation, your work will consist of the following: Read and understand the papers and text books related to the subject that you will present. Prepare a set of PowerPoint slides for an approximately one-course-long presentation. Select key ideas, concepts, and techniques, and focus on them.
Presentation Email your slides at least 1 week before the presentation. Email the main papers used for presentation. Each student has to present a specific topics, however it is possible to contribute in a common subject to extend the coverage of presentation.
Robocup Presentation Will address newcomers and interested students in the RoboCup, the scenario of autonomous soccer playing robots. Will overview RoboCup aims and rules. Will focus on topics ranging from hardware and kinematics to perception, localization and higher level behavior programming paradigms. Will provide the necessary theoretical and practical fundamentals for students.
Localization Kalman filter localization Monte Carlo localization Markov localization Comparison of localization methods Odometery Probabilistic Localization Positioning for mobile robots
Mobile Robot Motion Planning Collision Detection and Distance Computation algorithms Path Planning for a Point Robot Configuration Space Voronoi Roadmaps Probabilistic Roadmaps Visibility graphs
Methods for Representing Spatial Information Occupancy grids Potential fields Multi-scale elevation maps Environment exploration Map building Graph construction
Navigation Metric navigation Relative navigation Topological navigation Dealing with uncertainty Landmark based navigation Robot case study Locomotion Bayesian estimation
Control and Architecture Architecture Reactive control Planner-based Hybrid control Behavior-based control Control of wheeled mobile Hardware design of mobile robots Brooks subsumptions. Fuzzy control of mobile robots
Sensing Omni-directional vision Beacon localization Vision based navigation Laser range finder for mapping Sonar for mapping
Learning Reinforcement learning Genetic methods Neural networks Etc: Human-Robot Interfaces