Sensor Planning and Control in a Dynamic Environment

This paper presents an approach to the problem of controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots\u27 configuration is particularly important in the context of teams equipped with vision sensors since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem and a practical computational strategy, inspired by work on particle filtering, for implementing the approach. We extend our previous work by showing how modeled system dynamics and configuration space obstacles can be handled. These ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources

Systems simulations supporting NASA telerobotics

Two simulation and analysis environments have been developed to support telerobotics research at the Langley Research Center. One is a high-fidelity, nonreal-time, interactive model called ROBSIM, which combines user-generated models of workspace environment, robots, and loads into a working system and simulates the interaction among the system components. Models include user-specified actuator, sensor, and control parameters, as well as kinematic and dynamic characteristics. Kinematic, dynamic, and response analyses can be selected, with system configuration, task trajectories, and arm states displayed using computer graphics. The second environment is a real-time, manned Telerobotic Systems Simulation (TRSS) which uses the facilities of the Intelligent Systems Research Laboratory (ISRL). It utilizes a hierarchical structure of functionally distributed computers communicating over both parallel and high-speed serial data paths to enable studies of advanced telerobotic systems. Multiple processes perform motion planning, operator communications, forward and inverse kinematics, control/sensor fusion, and I/O processing while communicating via common memory. Both ROBSIM and TRSS, including their capability, status, and future plans are discussed. Also described is the architecture of ISRL and recent telerobotic system studies in ISRL

Active SLAM using model predictive control and attractor based exploration

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as Model Predictive Control (a.k.a. Receding Horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone. © 2006 IEEE

DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D* PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT

The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions

Dynamic update of a virtual cell for programming and safe monitoring of an industrial robot

A hardware/software architecture for robot motion planning and on-line safe monitoring has been developed with the objective to assure high flexibility in production control, safety for workers and machinery, with user-friendly interface. The architecture, developed using Microsoft Robotics Developers Studio and implemented for a six-dof COMAU NS 12 robot, established a bidirectional communication between the robot controller and a virtual replica of the real robotic cell. The working space of the real robot can then be easily limited for safety reasons by inserting virtual objects (or sensors) in such a virtual environment. This paper investigates the possibility to achieve an automatic, dynamic update of the virtual cell by using a low cost depth sensor (i.e., a commercial Microsoft Kinect) to detect the presence of completely unknown objects, moving inside the real cell. The experimental tests show that the developed architecture is able to recognize variously shaped mobile objects inside the monitored area and let the robot stop before colliding with them, if the objects are not too small

Compensating for model uncertainty in the control of cooperative field robots

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (p. 113-123).Current control and planning algorithms are largely unsuitable for mobile robots in unstructured field environment due to uncertainties in the environment, task, robot models and sensors. A key problem is that it is often difficult to directly measure key information required for the control of interacting cooperative mobile robots. The objective of this research is to develop algorithms that can compensate for these uncertainties and limitations. The proposed approach is to develop physics-based information gathering models that fuse available sensor data with predictive models that can be used in lieu of missing sensory information. First, the dynamic parameters of the physical models of mobile field robots may not be well known. A new information-based performance metric for on-line dynamic parameter identification of a multi-body system is presented. The metric is used in an algorithm to optimally regulate the external excitation required by the dynamic system identification process. Next, an algorithm based on iterative sensor planning and sensor redundancy is presented to enable field robots to efficiently build 3D models of their environment. The algorithm uses the measured scene information to find new camera poses based on information content. Next, an algorithm is presented to enable field robots to efficiently position their cameras with respect to the task/target. The algorithm uses the environment model, the task/target model, the measured scene information and camera models to find optimum camera poses for vision guided tasks. Finally, the above algorithms are combined to compensate for uncertainties in the environment, task, robot models and sensors. This is applied to a cooperative robot assembly task in an unstructured environment.(cont.) Simulations and experimental results are presented that demonstrate the effectiveness of the above algorithms on a cooperative robot test-bed.by Vivek Anand Sujan.Ph.D

Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation

Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "sim-to-real gap". We present a method for continuous improvement of modeling and control after deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that performs online system identification and optimal control simultaneously, using the incoming observations from the target environment in real time. To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters, using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adaptively schedule the optimization window in the future so that the optimized actions can be replenished faster than they are consumed, while staying as up-to-date with new sensor information as possible. The constant re-planning based on a constantly improved model allows the robot to swiftly adapt to the changing environment and utilize real-world data in the most sample-efficient way. Thanks to a fast differentiable physics simulator, the optimization for both system identification and control can be solved efficiently for robots operating in real time. We demonstrate our method on a set of examples in simulation and show that our results are favorable compared to baseline methods

Cooperative trajectory planning algorithm of USV-UAV with hull dynamic constraints

Efficient trajectory generation in complex dynamic environment stills remains an open problem in the unmanned surface vehicle (USV) domain. In this paper, a cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed, to ensure that USV can execute safe and smooth path in the process of autonomous advance in multi obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role as a flight sensor, and it provides real-time global map and obstacle information with lightweight semantic segmentation network and 3D projection transformation. And then an initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.Comment: 10 pages, 9 figure