Hybrid STAN: Identifying and managing combinatorial optimisation sub-problems in planning

It is well-known that planning is hard but it is less well-known how to approach the hard parts of a problem instance effectively. Using static domain analysis techniques we can identify and abstract certain combinatorial sub-problems from a planning instance, and deploy specialised technology to solve these sub-problems in a way that is integrated with the broader planning activities. We have developed a hybrid planning system (STAN4) which brings together alternative planning strategies and specialised algorithms and selects them according to the structure of the planning domain. STAN4 participated successfully in the AIPS-2000 planning competition. We describe how sub-problem abstraction is done, with particular reference to route-planning abstraction, and present some of the competition data to demonstrate the potential power of the hybrid approach

Identifying Critical Regions for Robot Planning Using Convolutional Neural Networks

abstract: In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP). In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners.Dissertation/ThesisMasters Thesis Computer Science 201

Hierarchical Manipulation for Constructing Free Standing Structures

abstract: In order for a robot to solve complex tasks in real world, it needs to compute discrete, high-level strategies that can be translated into continuous movement trajectories. These problems become increasingly difficult with increasing numbers of objects and domain constraints, as well as with the increasing degrees of freedom of robotic manipulator arms. The first part of this thesis develops and investigates new methods for addressing these problems through hierarchical task and motion planning for manipulation with a focus on autonomous construction of free-standing structures using precision-cut planks. These planks can be arranged in various orientations to design complex structures; reliably and autonomously building such structures from scratch is computationally intractable due to the long planning horizon and the infinite branching factor of possible grasps and placements that the robot could make. An abstract representation is developed for this class of problems and show how pose generators can be used to autonomously compute feasible robot motion plans for constructing a given structure. The approach was evaluated through simulation and on a real ABB YuMi robot. Results show that hierarchical algorithms for planning can effectively overcome the computational barriers to solving such problems. The second part of this thesis proposes a deep learning-based algorithm to identify critical regions for motion planning. Further investigation is done whether these learned critical regions can be translated to learn high-level landmark actions for automated planning.Dissertation/ThesisMasters Thesis Computer Science 201

Proof obligations as a support tool for efficient process management in the field of production planning and scheduling

Production planning and scheduling is one of the most important business processes that significantly influence the performance of manufacturing companies. There are many information systems supporting production planning and scheduling and some of them are based on very sophisticated planning algorithms. Despite this fact, many companies still face serious problems even while using professional software tools for production planning and scheduling. Obviously, a lot of other changes in form of process innovations are required. This paper deals with the problem of process management in the field of production planning and scheduling. Our study explains reasons for low performance of advanced technologies and provides solution in form of system model of key factors affecting the efficiency of planning software. Research part is based on the study conducted within Czech manufacturing companies in form of questionnaire-based investigation combined with interviews. Proposed solution is extended to the abstract mathematical model based on proof obligations which prove or disprove the correctness of intended algorithms. Our study provides basic example of such an abstract model and describes its functionality and influence to proper production planning and scheduling. It will be processed to the form of complex expert system based on Event B method in the future

Planning With Adaptive Dimensionality

Modern systems, such as robots or virtual agents, need to be able to plan their actions in increasingly more complex and larger state-spaces, incorporating many degrees of freedom. However, these high-dimensional planning problems often have low-dimensional representations that describe the problem well throughout most of the state-space. For example, planning for manipulation can be represented by planning a trajectory for the end-effector combined with an inverse kinematics solver through obstacle-free areas of the environment, while planning in the full joint space of the arm is only necessary in cluttered areas. Based on this observation, we have developed the framework for Planning with Adaptive Dimensionality, which makes effective use of state abstraction and dimensionality reduction in order to reduce the size and complexity of the state-space. It iteratively constructs and searches a hybrid state-space consisting of both abstract and non-abstract states. Initially the state-space consists only of abstract states, and regions of non-abstract states are selectively introduced into the state-space in order to maintain the feasibility of the resulting path and the strong theoretical guarantees of the algorithm---completeness and bounds on solution cost sub-optimality. The framework is able to make use of hierarchies of abstractions, as different abstractions can be more effective than others in different parts of the state-space. We have extended the framework to be able to utilize anytime and incremental graph search algorithms. Moreover, we have developed a novel general incremental graph search algorithm---tree-restoring weighted A*, which is able to minimize redundant computation between iterations while efficiently handling changes in the search graph. We have applied our framework to several different domains---navigation for unmanned aerial and ground vehicles, multi-robot collaborative navigation, manipulation and mobile manipulation, and navigation for humanoid robots

Dynamic Multi-Heuristic A*

Abstract—Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically provide good solution qualities and are not affected by narrow passages. However, in the absence of a good heuristic or when there are deep local minima in the heuristic, they suffer from the curse of dimensionality. In this work, our primary contribution is a method for dynamically generating heuristics, in addition to the original heuristic(s) used, to guide the search out of local minima. With the ability to escape local minima easily, the effect of dimensionality becomes less pronounced. On the theoretical side, we provide guarantees on completeness and bounds on suboptimality of the solution found. We compare our proposed method with the recently published Multi-Heuristic A * search, and the popular RRT-Connect in a full-body mobile manipulation domain for the PR2 robot, and show its benefits over these approaches. I