Online Searching with an Autonomous Robot

We discuss online strategies for visibility-based searching for an object hidden behind a corner, using Kurt3D, a real autonomous mobile robot. This task is closely related to a number of well-studied problems. Our robot uses a three-dimensional laser scanner in a stop, scan, plan, go fashion for building a virtual three-dimensional environment. Besides planning trajectories and avoiding obstacles, Kurt3D is capable of identifying objects like a chair. We derive a practically useful and asymptotically optimal strategy that guarantees a competitive ratio of 2, which differs remarkably from the well-studied scenario without the need of stopping for surveying the environment. Our strategy is used by Kurt3D, documented in a separate video.Comment: 16 pages, 8 figures, 12 photographs, 1 table, Latex, submitted for publicatio

Autonomous Robot Microcontroller Based Obstacle Detection at C52

Robot obstacle detection is a form of mobile robot's mission is to follow a track or path of the wall that has been determined. In the design and implementation, the problems to be solved is a robot vision system, hardware architecture (hardware) which includes electronic and mechanical devices, and software organizations (software) for the base of knowledge and control in real time. The purpose of this final task is to design and implement a Automomous Robot Microcontroller Based Obstacle Detection AT C52. Software organization of events using a base time is set to save the use of time. Source of knowledge of the encoding robot action must be done by robots based on information from sensors

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment

Autonomous Robot Sphere

The Autonomous Robot Sphere is an interactive robot toy meant to entertain kids. The robot will locate its target and execute algorithms to autonomously evade or chase a child. The sphere will contain a platform equipped with four omni-wheels, which will allow the sphere to maneuver and change direction almost instantaneously. The robot will be configured to maintain a fixed distance from the transmitter, allowing it to chase or evade the child in response to their movement. The primary advantage of our design lies in its capability to quickly adapt to changes in direction

Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an a-priori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the data-flow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility is calculated, an empirical and non-invasive characterization of the inherent objectives of the software designers. By comparing off-line (a-priori) utility with on-line (deployed system) utility, we show, using a small but real ROS example, that it's possible to monitor a performance criterion and relate violations of the criterion to parts of the software. The software is then patched using automated software repair techniques and evaluated against the original off-line utility.Comment: IEEE Workshop on Assured IEEE Workshop on Assured Autonomous Systems, May, 202