In this article, we present the main features of the dynamic rapidly-exploring generalized bur tree (DRGBT) algorithm, a sampling-based planner for dynamic environments. We provide a detailed time analysis and appropriate scheduling to facilitate a real-time operation. To this end, an extensive analysis is conducted to identify the time-critical routines and their dependence on the number of obstacles. Furthermore, information about the distance to obstacles is used to compute a structure calleddynamic expanded bubbleof free configuration space, which is then utilized to establish sufficient conditions for a guaranteed safe motion of the robot while satisfying all kinematic constraints. An extensive comparative study is conducted to compare the proposed algorithm to competing state-of-the-art methods. Finally, an experimental study on a real robot is carried out covering a variety of scenarios including those with human presence. The results show the effectiveness and feasibility of real-time execution of the proposed motion planning algorithm within a typical sensor-based arrangement, using cheap hardware and sequential architecture, without the necessity for GPUs or heavy parallelization.

This paper presents an effective approach to enable performance improvement in human-robot collaboration scenarios. The problem is tackled from the perspective of speed and separation monitoring principle, which stems from the recently instituted safety standard. The proposed approach attempts to seek for performance gains, measured by the speed-up of the production cycle, without compromising the safety constraints consistent with the standard. The approach is based on the notion of braking surface - an abstraction of the swept volume described by the manipulator during braking motion. We address two types of braking behavior: general and path-consistent. In both cases, the braking surface can be evaluated in a receding horizon manner. The robot velocity is continuously scaled such that, in case of a controlled stop, the corresponding volume spanned by the robot (braking surface) does not interfere with the surrounding obstacles. The approach is entirely kinematic and does not require the knowledge of the robot's dynamic model. Simulation study indicates that the pro-posed approach offers performance improvements compared to other state of the art methods. Moreover, the experiments demonstrate the real-time applicability of the method with the real robot in human-shared environment.

This paper deals with safe human-robot collaboration in the context of speed and separation monitoring paradigm. The core of the approach is to continuously track the separation distance between the robot and the human. The robot speed is then adjusted according to the perceived distance so that it will be able to stop before eventually come into contact with the human. We present an approach that aims at maximizing the productivity of the robot, i.e., its speed, while keeping the prescribed safety requirements satisfied. The method is based on explicit representation of danger zones – regions around the robot, where safety requirements are violated. The motion is then generated such that the robot moves as fast as possible, while its danger zone still does not collide with human operators. The approach is validated within an experimental study. Note to Practitioners—This article was motivated by the problem of maximizing productivity of the robotic manipulator while ensuring the safety of human collaborator. The increase in productivity is achieved by a faster traversal of predefined paths without compromising the safety of the human, which is specifically defined by industrial standard. The approach requires limited knowledge on robot’s dynamical properties. More precisely, we only need the braking time as a “lumped” representation of robot’s inertia. The underlying optimization problem is conveniently resolved by introducing danger zones that allow for intuitive visualization and geometrical representation of the regions around the robot that must be avoided. On the other hand, the method assumes the representation of humans via typical geometric primitives, which can be obtained using of-the-shelf depth perception systems. The solution to the problem reduces to a repeated collision checking between danger zones and the human. Such an approach turns out to be suitable for real-time implementation due to availability of fast and efficient collision checking algorithms/libraries.

In this paper, we present a novel algorithm – DRGBT (Dynamic Rapidly-exploring Generalized Bur Tree), intended for motion planning in dynamic environments. The main idea behind DRGBT lies in a so-called adaptive horizon, consisting of a set of prospective target nodes that belong to a predefined $\mathcal{C}$-space path, which originates from the current node. Each node is assigned a weight that depends on relative distances and captured changes in the environment. The algorithm continuously uses a suitable horizon assessment to decide when to trigger the replanning procedure. A comprehensive simulation study is performed, covering a variety of manipulators, where DRGBT is compared to a state-of-the-art algorithm. Results indicate some promising features of the proposed method.

We present a simple method to quickly explore C-spaces of robotic manipulators and thus facilitate path planning. The method is based on a novel geometrical structure called generalized bur. It is a star-like tree, rooted at a given point in free C-space, with an arbitrary number of guaranteed collision-free edges computed using distance information from the workspace and simple forward kinematics. Generalized bur captures large portions of free C-space, enabling accelerated exploration. The workspace is assumed to be decomposable into a finite set of (possibly overlapping) convex obstacles. When plugged in a suitable RRT-like planning algorithm, generalized burs enable significant performance improvements, while at the same time enabling exact collision-free paths.

In this paper, we approach the problem of ensuring safety requirements within human-robot collaborative scenarios. The safety requirements considered herein are consistent with the paradigm of speed and separation monitoring. In such a setup, safety guarantees for human operators usually imply limited robot velocities and/or significant distance margins, which in turn may have adverse effects regarding the productivity of the robot. In this paper, we propose a novel approach that minimally affects the productivity while being consistent with such a safety prescription. A comprehensive simulation study shows that our method outperforms the current state of the art algorithm.

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

This paper presents a novel method for using volumetric information in the C-space for generating collision-free paths for robotic manipulators in the presence of obstacles. The method is based on the RRT paradigm, i.e., incrementally building trees in C-space in order to connect the initial configuration to the final one. Bubbles of free C-space are used within a carefully modified extend procedure to add lengthy edges to the tree. In particular, we propose an expanded version of the bubble, which enables even more efficient exploration. The method is validated and compared to other planning algorithms within a simulation study that includes several types of manipulators and obstacle scenarios.

This paper presents a new approach to C-space exploration and path planning for robotic manipulators using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed using the distance information in the workspace. We show how the same distance information can be used to compute the bur that can reach substantially beyond the boundary of the bubble. It is shown how burs can be used to form a rapidly exploring bur tree (RBT): a space-filling tree that resembles RRT. Such a structure can easily be used within a suitably tailored path planning algorithm. Simulation study shows how the RBT-based algorithm outperforms the classical RRT-based method.

This paper presents a new approach to C-space exploration and optimal path planning for robotic manipulators and planar scenarios using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed using the distance information in the workspace. It is previously shown how burs can be used to form a rapidly exploring bur tree (RBT): a space-filling tree that resembles RRT. Now, we exploit the burs of free C-space approach to develop a new algorithm called RBT*, which is, like RRT* algorithm, provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Burs of free space offer better performance and faster convergence because it enables faster exploration of free space.

This paper presents a synergistic approach to danger assessment and safety-oriented control of articulated robots that are based on a quantity called danger field. This quantity captures the state of the robot as a whole and indicates how dangerous the current posture and velocity of the robot are to the objects in the environment. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the robot's workspace using measurements from the proprioceptive sensors. Furthermore, the danger field can be expressed in the closed form, which enables its fast computation. Apart from being a pure safety assessment, the danger field provides a natural prelude to safety-oriented control strategy. Namely, the information about the danger field can easily be fed back to shape standard control schemes in order to make the motion of the robot safer to the environment. The proposed method is validated through simulations and experiments.

This paper presents a new control law for robotic manipulators in unstructured environments which guarantees the achievement of the goal position without incurring in local minima. The passivity of the closed-loop system renders this control scheme well-suited for human-robot coexistence, especially when the robot is supposed to share its workspace with humans. The given control law has been implemented and experimentally tested in a realistic scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment without any offline planning phase.

In this paper we define a mathematical notion of ectropy for classifying diversity measures in terms of the extent to which they tend to penalize point collocation, we investigate the advantages and disadvantages of several known measures and we propose some novel ones. In particular, we introduce a measure based on Euclidean minimum spanning trees, a class of power mean based measures and three measures based on discrepancy from uniform distribution. All considered measures are tested and compared on a large set of random and structured populations. Special attention is also devoted to the complexity of computing the measures. The measure based on Euclidean minimum spanning trees turns out to be the most promising one in terms of the tradeoff between the computational complexity and the ectropic behavior.

A novel method to obtain safe paths for robotic manipulators is presented. It emulates the probabilistic roadmap planner by trying to establish a collision-free path that connects the start and the goal configuration via samples that belong to a quasi-random, low discrepancy sequence. The path construction is guided by a search algorithm with a heuristic function that includes a suitably tailored safety estimation. The measure of safety is based on the danger field - a recently proposed safety assessment for human-robot interaction. Thus, the planner provides not only collision-free paths, but strives for safer ones. In order to decrease the expected number of collision checks, we propose a novel method for testing whether local paths are collision-free or not. The method combines the standard binary collision checking with the concept of bubbles of free configuration space.

This paper presents a novel method for evaluating the danger within the environment of a robot manipulator. It is based on the introduced concept of kinetostatic danger field, a quantity that captures the complete state of the robot - its configuration and velocity. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the workspace using measurements from the proprioceptive sensors. Moreover, all the computation can be performed in closed form, yielding compact algebraic expressions that allow for real time applications. The danger field is not only a meaningful indicator about the risk in the vicinity of the robot, but can also be fed back within control skills that implement some well known safety strategies like collision avoidance and virtual impedance control, provided that some environment perception is available in order to determine the points where the field should be computed. Kinematic redundancy for simultaneous task performance and danger minimization can be exploited. The methodology described in the paper is supported with simulation results.

We propose a novel method of path planning for robotic manipulators that is based on the tree expansion via bubbles of free configuration space. The algorithm is designed to yield collision-free paths that also tend to minimize a certain danger criterion. This is achieved by embedding a suitably tailored heuristics within the algorithm. For that purpose we use a recently proposed safety assessment based on the concept of the danger field - an easily computable quantity that captures the complete kinematic behavior of the manipulator. Under the assumption that a systematic graph search technique dictates the tree growth, we prove the algorithm's completeness.

This paper deals with both introducing novel technique of calculating population diversity and analyzing the existing ones. This motivation to investigate new methods of determining population diversity lies in significant disadvantages of commonly used techniques, particularly the ones that operate in a parameter space. The problem with these methods is that they can produce inexact information about population state, e.g. indicate high diversity when it is far from being high. For the purpose of eliminating these problems, new diversity mechanisms are investigated. The main idea was to use the information that is contained in the matrix with all mutual distances between individuals. New mechanism can be employed within a standard paralell search algorithms (wheter as analyzing or guiding mechanism), or in general, as a mechanism for determining how well does the finite set of points sample a compact region of space.