Future robotic space missions will employ a precision soft-landing capability that will enable exploration of previously inaccessible sites that have strong scientific significance. To enable this capability, a fully autonomous onboard system that identifies and avoids hazardous features such as steep slopes and large rocks is required. Such a system will also provide greater functionality in unstructured terrain to unmanned aerial vehicles. This paper describes an algorithm for landing hazard avoidance based on images from a single moving camera. The core of the algorithm is an efficient application of structure from motion to generate a dense elevation map of the landing area. Hazards are then detected in this map and a safe landing site is selected. The algorithm has been implemented on an autonomous helicopter testbed and demonstrated four times resulting in the first autonomous landing of an unmanned helicopter in unknown and hazardous terrain.

Robotic lighter-than-air vehicles, or aerobots, are strategic surveying and instrument deployment platforms for the exploration of planets and moons with an atmosphere, such as Venus, Mars and Titan. Aerobots are characterized by modest power requirements, extended mission duration and long traverse capabilities, and the ability to transport and deploy scientific instruments and in-situ laboratory facilities over vast distances. With the arrival of the Huygens probe at Saturn's moon Titan in early 2005, there is considerable interest in a follow-on mission that would use a substantially autonomous aerobot to explore Titan's surface. In this paper, we discuss first steps towards the development of an autonomy architecture and a core set of perception, reasoning and control technologies for a future Titan aerobot. We provide an overview of the autonomy architecture, which integrates perception-based flight planning and control, vehicle health monitoring and safing, long-range mission planning and monitoring, and vision-based science site surveying. We describe the JPL aerobot and the onboard avionics architecture testbeds, and conclude with results from initial teleoperated test flights.

We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad) and navigation to the target, from an arbitrary initial position and orientation. We use vision for precise target detection and recognition, and a combination of vision and Global Positioning System for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition, and control algorithms are accurate, robust, and repeatable.

Numerous upcoming NASA missions need to land safely and precisely on planetary bodies. Accurate and robust state estimation during the descent phase is necessary. Towards this end, we have developed an approach for improved state estimation by augmenting traditional inertial navigation techniques with image-based motion estimation (IBME). A Kalman filter that processes rotational velocity and linear acceleration measurements provided from an inertial measurement unit has been enhanced to accommodate relative pose measurements from the IBME. In addition to increased state estimation accuracy, IBME convergence time is reduced while robustness of the overall approach is improved. The methodology is described in detail and experimental results with a 5 DOF gantry testbed are presented.

We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The helicopter is required to navigate from an initial position to a final position in a partially known environment based on GPS and vision, locate a landing target (a helipad of a known shape) and land on it. We use vision for precise target detection and recognition. The helicopter updates its landing target parameters based on vision and uses an on-board behavior-based controller to follow a path to the landing site. We present results from flight trials in the field which demonstrate that our detection, recognition and control algorithms are accurate and repeatable.

A control system architecture is described for an autonomous flying vehicle. The vehicle, equipped with fourteen sensors, uses a model helicopter as an airframe. The control system utilizes these sensors to: remain aloft and in stable flight; navigate to a target; and manipulate a physical object. The overall approach to the problem is based on a behavioral paradigm. The key contribution of this paper is the demonstration of a situated agent under these severe circumstances; as the craft is airborne, it is in constant risk of crashing. Unlike terrestrial mobile robots, the craft must constantly make sound decisions to maintain its integrity.