A hierarchic approach for path planning in virtual reality.

This work considers path-planning processes for manipu- lation tasks such as assembly, maintenance or disassem- bly in a virtual reality (VR) context. The approach con- sists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on semantic, topological and geometric representations of the environment and the planning process is split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a haptic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the users intention and computes in real-time a new path starting from the users guess. Experiments illustrate the different aspects of the approach: multi-representation of the en- vironment, path planning process, users intent prediction and control sharing

Automated Planning Volume Contouring Algorithm For Adjuvant Brachytherapy Treatment Planning In Sarcoma

A mathematical contouring algorithm that automatically determines the planning volume of a sarcoma prior to designing a brachytherapy treatment plan. The algorithm, utilizing computational geometry, numerical interpolation and artificial intelligence (AI) techniques, returns the planning volume in digitized and graphical forms in a matter of minutes. Such an automatic procedure reduces labor time and provides a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume allows for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumor control and minimize normal tissue complications.Georgia Tech Research Corporatio

Interactive logical analysis of planning domains

Humans exhibit a significant ability to answer a wide range of questions about previously unencountered planning domains, and leverage this ability to construct “general-purpose\u27\u27 solution plans for the domain. The long term vision of this research is to automate this ability, constructing a system that utilizes reasoning to automatically verify claims about a planning domain. The system would use this ability to automatically construct and verify a generalized plan to solve any planning problem in the domain. The goal of this thesis is to start with baseline results from the interactive verification of claims about planning domains and develop the necessary knowledge representation and reasoning methods to progressively reduce the amount of human interaction required. To achieve this goal, a representation of planning domains in a class-based logic syntax was developed. A novel proof assistant was then used to perform semi-automatic machine analysis of two benchmark planning domains: Blocksworld and Logistics. This analysis was organized around the interactive formal verification of state invariants and specifications of the state-change effects of handwritten recursive program-like generalized plans. The human interaction required for these verifications was metered and qualitatively characterized. This characterization motivated several algorithmic changes to the proof assistant resulting in significant savings in the interactions required. A strict limit was enforced on the time spent by the base reasoner in response to user queries; interactions taking longer were studied to direct improvements to the inference engine\u27s efficiency. A complete account of these changes is provided

A multi-layer approach for interactive path planning control.

This work considers path-planning processes for manipulation tasks such as assembly, maintenance or disassembly in a Virtual Reality (VR) context. The approach consists in providing a collaborative system associating a user immersed in VR and an automatic path planning process. It is based on semantic, topological and geometric representations of the environment and the planning process is split in two phases: coarse and fine planning. The automatic planner suggests a path to the user and guides him trough a haptic device. The user can escape from the proposed solution if he wants to explore a possible better way. In this case, the interactive system detects the user’s intention in real-time and computes a new path starting from the user’s guess. Experiments illustrate the different aspects of the approach: multi-representation of the environment, path planning process, user’s intent prediction and control sharing

A New Tool for Rectangular Dualization

OcORD is a software tool for rectangular dualization. Rectangular dualization is a dual representation of a plane graph introduced in the early seventies. It proved to be effective in applications such as architectural space planning and VLSI floorplanning. However, not all plane graphs admit a rectangular dual, which imposes severe limitations on its use in other applications. OcORD aims at freeing rectangular dualization from such restrictions and proving its effectiveness in graph visualization. This is achieved in two ways. Firstly, OcORD features a new linear-time algorithm creating a rectangular dual of any plane graph. Secondly, it shows how nice drawings of a graph can be easily obtained from its rectangular dual. Finally, the automatic generation of a Virtual World through rectangular dualization is described. [DOI: 10.1685/CSC09301] About DO

Optimal temporal logic control of autonomous vehicles

Thesis (Ph.D.)--Boston UniversityTemporal logics, such as Linear Temporal Logic (LTL) and Computation Tree Logic (CTL), are extensions of propositional logic that can capture temporal relations. Even though temporal logics have been used in model checking of finite systems for quite some time, they have gained popularity as a means for specifying complex mission requirements in path planning and control synthesis problems only recently. This dissertation proposes and evaluates methods and algorithms for optimal path planning and control synthesis for autonomous vehicles where a high-level mission specification expressed in LTL (or a fragment of LTL) must be satisfied. In summary, after obtaining a discrete representation of the overall system, ideas and tools from formal verification and graph theory are leveraged to synthesize provably correct and optimal control strategies. The first part of this dissertation focuses on automatic planning of optimal paths for a group of robots that must satisfy a common high level mission specification. The effect of slight deviations in traveling times on the behavior of the team is analyzed and methods that are robust to bounded non-determinism in traveling times are proposed. The second part focuses on the case where a controllable agent is required to satisfy a high-level mission specification in the presence of other probabilistic agents that cannot be controlled. Efficient methods to synthesize control policies that maximize the probability of satisfaction of the mission specification are presented. The focus of the third part is the problem where an autonomous vehicle is required to satisfy a rich mission specification over service requests occurring at the regions of a partitioned environment. A receding horizon control strategy that makes use of the local information provided by the sensors on the vehicle in addition to the a priori information about the environment is presented. For all of the automatic planning and control synthesis problems that are considered, the proposed algorithms are implemented, evaluated, and validated through experiments and/or simulations

Smooth path planning with Pythagorean-hodoghraph spline curves geometric design and motion control

This thesis addresses two significative problems regarding autonomous systems, namely path and trajectory planning. Path planning deals with finding a suitable path from a start to a goal position by exploiting a given representation of the environment. Trajectory planning schemes govern the motion along the path by generating appropriate reference (path) points. We propose a two-step approach for the construction of planar smooth collision-free navigation paths. Obstacle avoidance techniques that rely on classical data structures are initially considered for the identification of piecewise linear paths that do not intersect with the obstacles of a given scenario. In the second step of the scheme we rely on spline interpolation algorithms with tension parameters to provide a smooth planar control strategy. In particular, we consider Pythagorean\u2013hodograph (PH) curves, since they provide an exact computation of fundamental geometric quantities. The vertices of the previously produced piecewise linear paths are interpolated by using a G1 or G2 interpolation scheme with tension based on PH splines. In both cases, a strategy based on the asymptotic analysis of the interpolation scheme is developed in order to get an automatic selection of the tension parameters. To completely describe the motion along the path we present a configurable trajectory planning strategy for the offline definition of time-dependent C2 piece-wise quintic feedrates. When PH spline curves are considered, the corresponding accurate and efficient CNC interpolator algorithms can be exploited

Automatic generation of robot and manual assembly plans using octrees

This paper aims to investigate automatic assembly planning for robot and manual assembly. The octree decomposition technique is applied to approximate CAD models with an octree representation which are then used to generate robot and manual assembly plans. An assembly planning system able to generate assembly plans was developed to build these prototype models. Octree decomposition is an effective assembly planning tool. Assembly plans can automatically be generated for robot and manual assembly using octree models. Research limitations/implications - One disadvantage of the octree decomposition technique is that it approximates a part model with cubes instead of using the actual model. This limits its use and applications when complex assemblies must be planned, but in the context of prototyping can allow a rough component to be formed which can later be finished by hand. Assembly plans can be generated using octree decomposition, however, new algorithms must be developed to overcome its limitations

On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

The Use of Proof Planning for Cooperative Theorem Proving

AbstractWe describebarnacle: a co-operative interface to theclaminductive theorem proving system. For the foreseeable future, there will be theorems which cannot be proved completely automatically, so the ability to allow human intervention is desirable; for this intervention to be productive the problem of orienting the user in the proof attempt must be overcome. There are many semi-automatic theorem provers: we call our style of theorem provingco-operative, in that the skills of both human and automaton are used each to their best advantage, and used together may find a proof where other methods fail. The co-operative nature of thebarnacleinterface is made possible by the proof planning technique underpinningclam. Our claim is that proof planning makes new kinds of user interaction possible.Proof planning is a technique for guiding the search for a proof in automatic theorem proving. Common patterns of reasoning in proofs are identified and represented computationally as proof plans, which can then be used to guide the search for proofs of new conjectures. We have harnessed the explanatory power of proof planning to enable the user to understand where the automatic prover got to and why it is stuck. A user can analyse the failed proof in terms ofclam's specification language, and hence override the prover to force or prevent the application of a tactic, or discover a proof patch. This patch might be to apply further rules or tactics to bridge the gap between the effects of previous tactics and the preconditions needed by a currently inapplicable tactic