Karl Iagnemma

The DARPA Urban Challenge

This volume, edited by Martin Buehler, Karl Iagnemma and Sanjiv Singh, presents a unique and comprehensive collection of the scientific results obtained by finalist teams that participated in the DARPA Urban Challenge in November 2007, in the mock city environment of the George Air Force base in Victorville, California. This book is the companion of a previous volume by the same editors which was devoted to the Grand Challenge, which took place in the Nevada desert during October 2005, and was the second in the series of autonomous vehicle races sponsored by DARPA. The Urban Challenge demonstrated how cutting-edge perception, control, and motion planning techniques can allow intelligent autonomous vehicles not only to travel significant distances in off-road terrain, but also to operate in urban scenarios. Beyond the value for future military applications--which motivated DARPA to sponsor the race--the expected impact in the commercial sector for automotive manufacturers is equally, if not more, important: autonomous sensing and control constitute key technologies for vehicles of the future, and might help save thousands of lives that are now lost in traffic accidents. As with the previous STAR volume, the original papers collected in this book were initially published in special issues of the Journal of Field Robotics. Our series is proud to collect them in an archival publication as a special STAR volume!

An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

This paper formulates the vehicle navigation task as a constrained optimal control problem with constraints bounding a traversable region of the environment. A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor and uses this trajectory to establish the minimum threat posed to the vehicle given its current state and driver inputs. Based on this threat assessment, the level of controller intervention required to prevent departure from the traversable corridor is calculated and driver/controller inputs are scaled accordingly. Simulated and experimental results are presented to demonstrate multiple threat metrics and configurable intervention laws.

Vibration-based terrain classification for planetary exploration rovers

Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rovers safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.

On-line terrain parameter estimation for planetary rovers

Future planetary exploration missions will require rovers to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper an on-line estimation method that identifies key terrain parameters using on-board rover sensors is presented. These parameters can be used for accurate traversability prediction or in a traction control algorithm. These parameters are also valuable indicators of planetary surface soil composition. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for loose sand.

Traction Control of Wheeled Robotic Vehicles in Rough Terrain with Application to Planetary Rovers

Mobile robots are being developed for high-risk missions in rough terrain situations, such as planetary exploration. Here, a rough-terrain control methodology is presented that exploits the actuator redundancy found in multiwheeled mobile robot systems to improve ground traction and reduce power consumption. The algorithm optimizes individual wheel torque based on multiple optimization criteria, which are a function of the local terrain profile. A key element of the method is to be able to include estimates of wheel-terrain contact angles and soil characteristics. A method using an extended Kalman filter is presented for estimating these angles using simple on-board sensors. Simulation and experimental results for a micro-rover traversing challenging terrain demonstrate the effectiveness of the algorithm.

Design and Analysis of a Robust, Low-cost, Highly Articulated manipulator enabled by jamming of granular media

Hyper-redundant manipulators can be fragile, expensive, and limited in their flexibility due to the distributed and bulky actuators that are typically used to achieve the precision and degrees of freedom (DOFs) required. Here, a manipulator is proposed that is robust, high-force, low-cost, and highly articulated without employing traditional actuators mounted at the manipulator joints. Rather, local tunable stiffness is coupled with off-board spooler motors and tension cables to achieve complex manipulator configurations. Tunable stiffness is achieved by reversible jamming of granular media, which-by applying a vacuum to enclosed grains-causes the grains to transition between solid-like states and liquid-like ones. Experimental studies were conducted to identify grains with high strength-to-weight performance. A prototype of the manipulator is presented with performance analysis, with emphasis on speed, strength, and articulation. This novel design for a manipulator-and use of jamming for robotic applications in general-could greatly benefit applications such as human-safe robotics and systems in which robots need to exhibit high flexibility to conform to their environments.

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.

Spirit Mars Rover Mission: Overview and selected results from the northern Home Plate Winter Haven to the side of Scamander crater

Spirit Mars Rover Mission : Overview and selected results from the northern Home Plate Winter Haven to the side of Scamander crater

Rapid physics-based rough-terrain rover planning with sensor and control uncertainty

In future planetary exploration missions, rovers will be required to autonomously traverse challenging environments. Much of the previous work in robot motion planning cannot be successfully applied to the rough-terrain planning problem. A model-based planning method is presented in this paper that is computationally efficient and takes into account uncertainty in the robot model, terrain model, range sensor data, and rover path following errors. It is based on rapid path planning through the visible terrain map with a simple graph-search algorithm, followed by a physics-based evaluation of the path with a rover model. Simulation results are presented which demonstrate the effectiveness of the method presented.

Potential Field Navigation of High Speed Unmanned Ground Vehicles on Uneven Terrain

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.