Adam K. Rzepniewski

Control of Robotic Vehicles with Actively Articulated Suspensions in Rough Terrain

Future robotic vehicles will perform challenging tasks in rough terrain, such as planetary exploration and military missions. Rovers with actively articulated suspensions can improve rough-terrain mobility by repositioning their center of mass. This paper presents a method to control actively articulated suspensions to enhance rover tipover stability. A stability metric is defined using a quasi-static model, and optimized on-line. The method relies on estimation of wheel-terrain contact angles. An algorithm for estimating wheel-terrain contact angles from simple on-board sensors is developed. Simulation and experimental results are presented for the Jet Propulsion Laboratory Sample Return Rover that show the control method yields substantially improved stability in rough-terrain.

Reconfigurable robots for all-terrain exploration

While significant recent progress has been made in development of mobile robots for planetary surface exploration, there remain major challenges. These include increased autonomy of operation, traverse of challenging terrain, and fault-tolerance under long, unattended periods of use. We have begun work which addresses some of these issues, with an initial focus on problems of high risk access, that is, autonomous roving over highly variable, rough terrain. This is a dual problem of sensing those conditions which require rover adaptation, and controlling the rover actions so as to implement this adaptation in a well understood way (relative to metrics of rover stability, traction, power utilization, etc.). Our work progresses along several related technical lines: 1) development a fused state estimator which robustly integrates internal rover state and externally sensed environmental information to provide accurate configuration information; 2) kinematic and dynamical stability analysis of such configurations so as to determine predicts for a needed change of control regime (e.g., traction control, active c.g. positioning, rover shoulder stance/pose); 3) definition and implementation of a behavior-based control architecture and action-selection strategy which autonomously sequences multi-level rover controls and reconfiguration. We report on these developments, both software simulations and hardware experimentation. Experiments include reconfigurable control of JPSs Sample Return Rover geometry and motion during its autonomous traverse over simulated Mars terrain.

Cycle-to-Cycle Control of Multivariable Manufacturing Processes With Process Model Uncertainty

In-process closed-loop control of many manufacturing processes is often impractical owing to the impossibility or the prohibitively high cost of placing sensors and actuators necessary for in-process control. Such processes are usually left to statistical process control methods, which only identify problems without specifying solutions. Cycle-to-cycle control is a method for using feedback to improve product quality for processes that are inaccessible within a single processing cycle but can be changed between cycles. This type of control has the same objectives as run-by-run control. However, it is developed from a different point of view allowing easy analysis of the process’ transient closed-loop behavior due to changes in the target value or to output disturbances. Our previous work introduced cycle-to-cycle control for single input-single output processes and here it is extended to multiple input-multiple output processes. Gain selection, stability, and process variance amplification results are developed and compared with those obtained by previous researchers, showing good agreement. Then, the limitation of imperfect knowledge of the plant model is imposed. This is consistent with manufacturing environments that require minimal cost and number of tests in determining a valid process model. The effects of this limitation on system performance and stability are discussed. The theoretical results are applied to a novel, discrete-die sheet metal stretch-forming process. The classical, monolithic tool is replaced by a large number of small, separate pieces that can be reconfigured between cycles to approximate continuous shapes. Thus, each forming cycle can use a new input shape. The experimental system is an ideal candidate for the application of cycle-to-cycle control in a multivariable fashion. A linear process model is presented that includes the effects of single input-multiple output coupling. Experimental validation of variance amplification results for a sheet metal forming processes is presented with hundreds of inputs and outputs. While many controller designs could be considered a purely diagonal (decoupled) and a Linear Quadratic Regulator design are presented and discussed. Comparison between theory and experiments is provided, showing good agreement.Copyright