Simulating complex industrial manipulation tasks (e.g., assembly, disassembly and maintenance tasks) under strong geometric constraints in a virtual environment, requires the joint usage of task and path planning, not only to compute a sequence of primitive actions (i.e., a task plan) at task planning level to identify the order to manipulate different objects (e.g., assembly order), but also to generate and validate motions for each of these primitive actions in a virtual environment by computing valid collision-free paths for these actions at path planning level. Although task and path planning have been respectively welly discussed by artificial intelligence and robotic domain, the link between them still remains an open issue, in particular because path planning for a primitive action often uses purely geometric data. This purely geometric path planning suffers from the classical failures (i.e., high-possibility of failure, high processing time and low path relevance) of automated path planning techniques when dealing with complex geometric models. Thus, it can possibly lead to high computational time of the joint task and path planning process and can probably produce a poor implementation of a task plan. Instead of geometric data, involving higher abstraction level information related to a task to be performed in the path planning of a primitive action could lead to a better relevance of simulations. In this work, we propose an ontology-based approach to generate a specific path planning query for a primitive action, using a well-structured task-oriented knowledge model. This specific path planning query aims at obtaining an increased control on the path planning process of the targeted primitive action.

This paper focuses on interactive motion planning processes intended to assist a human operator when simulating industrial tasks in Virtual Reality. Such applications need motion planning on surfaces. We propose an original haptic path planning algorithm with contact, H-RRT-C, based on a RRT planner and a real-time interactive approach involving a haptic device for computer-operator authority sharing. Force feedback allows the human operator to keep contact consistently and provides the user with the feel of the contact, and the force applied by the operator on the haptic device is used to control the roadmap extension. Our approach has been validated through two experimental examples, and brings significant improvement over state of the art methods in both free and contact space to solve path-planning queries and contact operations such as insertion or sliding in highly constrained environments.

This work deals with interactive motion planning processes intended to assist a human operator when simulating industrial tasks such as assembly, maintenance or disassembly in Virtual Reality. Such applications need motion planning on surfaces. We propose an original interactive path planning algorithm with contact, I-RRT-C, based on a RRT-Connect approach. This algorithm is based on a real-time interactive approach allowing both an automatic motion planner and a human operator to jointly explore the workspace. A parameter balances the authority between the computer and the operator to reduce processing times. We improve the guidance by allowing to sample on the surfaces of obstacles. Our method allows to find a path in cluttered environments or to solve contact operations such as insertion or sliding tasks. Last, we present experimental results showing that our interactive path planner with contact brings a significant improvement over state of the art methods in both free and contact space.

This work deals with planning processes for the assistance to manipulation in order to simulate industrial tasks such as assembly, maintenance or disassembly in Virtual Reality. This paper presents a novel interactive path planning algorithm with contact based on an BiRRT approach. First, we propose a real-time interactive planner where both a computer and a human operator simultaneously search the workspace therefore largely speeding up the process. An authority sharing parameter can control the autonomy of the computer. Then we present a novel contact-space algorithm able to sample on the surface of obstacles. This method helps finding paths in cluttered environments or solving specific contact tasks such as insertion or sliding operations. We finish by presenting the results of our interactive path planner with contact through three examples showing significant improvement over usual methods in both free and contact space.

This work considers Virtual Reality (VR) applications dealing with objects manipulation (such as industrial product assembly, disassembly or maintenance simulation). For such applications, the operator performing the simulation can be assisted by path planning techniques from the robotics research field. A novel automatic path planner involving geometrical, topological and semantic information of the environment is proposed for the guidance of the user through a haptic device. The interaction allows on one hand, the automatic path planner providing assistance to the human operator, and on the other hand, the human operator to reset the whole planning process suggesting a better suited path. Control sharing techniques are used to improve the assisted manipulation ergonomics by dynamically balancing the automatic path planner authority according to the operator involvement in the task, and by predicting user's intent to integrate it as early as possible in the planning process.

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This paper presents a 3D navigation subsystem providing specific treatments needed for the perception and the navigation of an all-terrain mobile robot. This subsystem was developed and integrated in the global framework of the EDEN experimentation. After a brief description of this outdoor navigation experimentation, we describe the natural environment representations we use. Two important perception functions based on 3D data are involved here: fast construction of elevation maps and robot localization. We then describe the 3D motion planner dedicated to the navigation on rugged terrains. The current state of integration of the experiment is finally presented by the mean of experimental results obtained from the natural environment of the mobile robot ADAM.>

This paper deals with the perception subsystem of an autonomous mobile robot which must navigate in structured or semistructured environments. For intervention in high risk zones, mismatches between an a priori known model of the environment and the perceived scenes must be considered, as well as the possibility that the ground is not necessarily horizontal. The incremental building of a model used for localization tasks only is addressed. It contains the representations of particular 3-D features extracted from depth images acquired by a laser range finder. Perceiving the same features again allows reestimation of the positions of the robot and the localization features in the environment. The problem of using simultaneously both previous kind of localization models is particularly considered.

The authors consider the perception subsystem of an autonomous mobile robot which must be able to navigate in 3D terrain. They describe their approach to building a rough geometric model of a 3D terrain accounting for the locomotion capabilities of the vehicle, using a laser range finder. This model may be used as direct input for the robot's path planner. The terrain model relies on two grid-based representations: the local elevation map and the local navigation map. Both are incrementally built at arbitrary resolution using new interpolation and localization methods and other 3D vision techniques. The authors validate the proposed approach by presenting some comprehensive results using real range images of an indoor structured environment.>