Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of operating missions with such onboard autonomous capabilities has not been studied to a level of detail sufficient for consideration in mission concepts. These autonomy capabilities will require changes to current operations processes, practices, and tools. We have developed a case study to assess the changes needed to enable operators and scientists to operate an autonomous spacecraft by facilitating a common model between the ground personnel and the onboard algorithms. We assess the new operations tools and workflows necessary to enable operators and scientists to convey their desired intent to the spacecraft, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft. Mock-ups of these tools were used in a user study to understand the effectiveness of the processes and tools in enabling a shared framework of understanding, and in the ability of the operators and scientists to effectively achieve mission science objectives.

We propose a new particle filter that incorporates a model of costs when generating particles. The approach is motivated by the observation that the costs of accidentally not tracking hypotheses might be significant in some areas of state space, and next to irrelevant in others. By incorporating a cost model into particle filtering, states that are more critical to the system performance are more likely to be tracked. Automatic calculation of the cost model is implemented using an MDP value function calculation that estimates the value of tracking a particular state. Experiments in two mobile robot domains illustrate the appropriateness of the approach.

We propose a new particle filter that incorporates a model of costs when generating particles. The approach is motivated by the observation that the costs of accidentally not tracking hypotheses might be significant in some areas of state space, and next to irrelevant in others. By incorporating acost model into particle filtering, states that are more critical to the system performance are more likely to be tracked. Automatic calculation of the cost model is implemented using an MDP value function calculation thatestimates the value of tracking a particular state. Experiments in two mobile robot domains illustrate the appropriateness of the approach.

The 2001 Association for the Advancement of Artificial Intelligence Mobile Robot Exhibition provided an opportunity for AI researchers to interact and share ideas. Despite some difficulties with environment and timing, the primary objective of disseminating information was achieved. A short summary of each robot demonstrates the variety in form and function among the exhibitions.

A short summary of each robot demonstrates the variety in form and function among the exhibitions. Programming a robot is often a complex and Intelligence (AAAI) Mobile Robot involved task. By allowing the robot to learn and wide variety in behavior and form, the robots practice behaviors at run time, Darrin Bentivegna in the exhibition created a sense of the broad aims to create robots that can learn range of function in the robotic community. The exhibition has involving air hockey against a humanoid provided past AI researchers with new perspectives robot. After a human has specified some primitives and ideas.