Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine

Pose, posture, formation and contortion in kinematic systems

The concepts of pose, posture, formation and contortion are defined for serial, parallel and hybrid kinematic systems. Workspace and jointspace structure is examined in terms of these concepts. The inter-relationships of pose, posture, formation and contortion are explored for a range of robot workspace and jointspace types

Towards a Probabilistic Roadmap for Multi-robot Coordination

International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

International audienceIn this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches

Minimizing the probabilistic magnitude of active vision errors using genetic algorithm

Spatial quantization errors are resulted in digitization. The errors are serious when the size of the pixel is significant compared to the allowable tolerance in the object dimension on the image. In placing the active sensor to perform inspection, displacement of the sensors in orientation and location is common. The difference between observed dimensions obtained by the displaced sensor and the actual dimensions is defined as displacement errors. The density functions of quantization errors and displacement errors depend on the camera resolution and camera locations and orientations. We use genetic algorithm to minimize the probabilistic magnitude of the errors subject to the sensor constraints, such as the resolution, field-of-view, focus, and visibility constraints. Since the objective functions and the constraint functions are both complicated and nonlinear, traditional nonlinear programming may not be efficient and trapping at a local minimum may occur. Using crossover operations, mutation operations, and the stochastic selection in genetic algorithm, trapping can be avoided.published_or_final_versio