The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive. A modular fuzzy navigation method in changing and dynamic unstructured...

To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful robot configurations is learned to support faster planning. More generally, the algorit...

Motion planning is a fundamental problem in a number of application areas, including robotics, automation, and virtual reality. The performance of motion planning is largely affected by the underlying collision detection technique. In this paper we report the results of an experimental evaluation of several recent collision detection libraries within the context of motion planning for rigid and...

SOMMARIO/ABSTRACT In this paper we report on the extension of the classical HTN planner SHOP to plan in partially observable domains with uncertainty. Our algorithm PC-SHOP uses belief states to handle situations involving incomplete and uncertain information about the state of the world. Sensing and acting are integrated in the primitive actions through the use of a stochastic model. PC-SHOP i...

We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (ipc-4). This version, called mGPT, solves Markov Decision Processes specified in the ppddl language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where t...

Scheduling is integral to many real-world logistics problems. It can be as simple as catching the bus in the morning, or as complex as assembling a commercial airliner. While simple applications render scheduling tools trivial, these tools have not been widely adopted for complex scenarios either. The larger the scenario, the greater the temporal uncertainty throughout the system, and many sche...

Real-world planning problems often involve hundreds or even thousands of objects, straining the limits modern planners. In this work, we address challenge by learning to predict a small set objects that, taken together, would be sufficient for finding plan. We propose graph neural network architecture predicting object importance in single inference pass, thus incurring little overhead while gr...

Chapter 1 and 2 of [Lat91]: Chapter 1 is an overview of motion planning and some overview of planning algorithms, such as Roadmap, Cell Decomposition and Potential field. Chapter 2 gives mathematical description about configuration space and obstacles, especially about the manifold structure of configuration space (embedding, parameterization, chart, atlas, connectedness, path, metric definitio...

This paper describes ProCHiP, a planner that combines CBR with the techniques of decision-theoretic planning and HTN planning in order to deal with uncertain, dynamic large-scale real-world domains. We explain how plans are represented, generated and executed. Unlike in regular HTN planning, ProCHiP can generate plans in domains where there is no complete domain theory by using cases instead of...

Legged vehicles have attracted interest for many high-mobility applications, such as military troop support and logistics in rocky, steep, and forested terrain, scientific exploration of cliffs, mountains, and volcanoes on earth and other planets, and search and rescue. Humanoid robots have additional applications in homes and offices as personal assistants. But planning and controlling their m...