The exploitation of new fields of application in addition to traditional industrial production for robot manipulators (e.g. agriculture, human areas) requires extensions to the sensor as well as to the planning capabilities. Motion planning solely based on visual information performs poorly in cluttered environments since contacts with obstacles might be inevitable and thus a distinction between hard and soft objects has to be made. In our contribution we present a novel intrinsic tactile sensing module mounted on a multipurpose 9 DOF agricultural manipulator. With its innovative sensor arrangement we consider it to be a low-cost, easily manageable and efficient solution with a reasonable abstraction layer in comparison to complex torque sensing or tactile skins. The sensor provides information about the resulting force and torque. In the second part of our paper, the tactile information is used for minimizing contact forces while pursuing the end-effector tasks as long as reasonable. Hence, we present robust and efficient extensions to Resolved Motion Rate Control for real-time application. We introduce a general formulation providing control inputs in task-space, joint-space and nullspace. Thus, we design a suitable controller by feedback linearization and feed-forward terms. Results from real-world experiments show the potential of our approach. A discussion of the different control schemes completes the paper.
Abstract-Collision-free walking in cluttered environments is still an open issue for humanoids. Most current approaches use heuristics with large safety margins to plan the robot's motion. That way, the chance of collisions can be greatly reduced but the robot movements are limited artificially. In this context, we extend our framework for motion generation and whole-body collision-avoidance by an online predictive kinematic parameter evaluation and optimization: We propose to evaluate the initial parameter set describing the walking pattern by integrating the full kinematic model of the robot. In the model our local optimization technique for collision avoidance is taken into account. Initial parameter sets, which are kinematically infeasible due to kinematic limits or collisions can be identified and adapted before the motion is executed. Additionally, the parameter set is optimized according to a chosen cost function using a gradient method and the step time is adapted according to a desired mean velocity. The optimization method is applicable to different representations of the walking pattern. The method is presented with simulation results obtained with our multi-body simulation. The method is suitable for real-time control, since the optimization can be stopped if it exceeds a predetermined time budget. In that case, an executable but suboptimal result is used. The proposed procedure is executed before each step which makes it very reactive to changes in the environment or in the user input. We have also validated the real-time performance in experiments with our humanoid Lola.
The robustness of biped walking in unknown and uneven terrains is still a major challenge in research. Traversing such environments is usually solved through vision-based reasoning on footholds and feedback loops—such as ground force control. Uncertain terrains are still traversed slowly to keep inaccuracies in the perceived environment model low. In this article, we present a ground force-control scheme that allows for fast traversal of uneven terrain—including unplanned partial footholds—without using vision-based data. The approach is composed of an early-contact method, direct force control with an adaptive contact model, and a strategy to adapt the center of mass height based on contact force data. The proposed method enables the humanoid robot Lola to walk over a complex uneven terrain with 6 cm variation in ground height at a walking speed of 0.5 m/s. We consider our work a general improvement on the robustness to terrain uncertainties caused by inaccurate or even lacking information on the environment.
Autonomous navigation in complex environments featuring obstacles, varying ground compositions, and external disturbances requires real-time motion generation and stabilization simultaneously. In this paper, we present and evaluate a strategy for rejection of external disturbances and real-time motion generation in the presence of obstacles and non-flat ground. We propose different solutions for combining the associated algorithms and analyze them in simulations. The promising method is validated in experiments with our robot Lola. We found a hierarchical approach to be effective for solving these complex motion generation problems, because it allows us to decompose the problem into sub-problems that can be tackled separately at different levels. This makes the approach suitable for real-time applications and robust against perturbations and errors. Our results show that real-time motion planning and disturbance rejection can be combined to improve the autonomy of legged robots.
Abstract-In order to achieve fully autonomous humanoid navigation, environment perception must be both fast enough for real-time planning in dynamic environments and robust against previously unknown scenarios. We present an open source, flexible and efficient vision system that represents dynamic environments using simple geometries. Based only on onboard sensing and 3D point cloud processing, it approximates objects using swept-sphere-volumes while the robot is moving. It does not rely on color or any previous models or information. We demonstrate the viability of our approach by testing it on our human-sized biped robot Lola, which is able to avoid moving obstacles in real-time while walking at a set speed of 0.4m/s and performing whole-body collision avoidance.
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