Collision Avoidance For Mobile Robots Research Articles

Mobile Robot Collision Avoidance Based on Deep Reinforcement Learning with Motion Constraints

Mobile Robot Collision Avoidance Based on Deep Reinforcement Learning with Motion Constraints

Edge-Weighted Consensus-Based Formation Control with Collision Avoidance for Mobile Robots Based on Multi-Strategy Mutation Differential Evolution

An edge-weighted consensus-based formation control strategy is presented for mobile robots. In the edge-weighted strategy, a desired formation pattern is achieved by adjusting gain weights related to the distance between robots. Moreover, the edge-weighted formation control exploits the properties of weighted graphs to allow the formation to rotate and adapt its shape to avoid collision among robots. However, formation patterns are commonly defined by biases with respect to the centroid of the consensus rather than gain weights. This work proposes to optimize the gain weights in edge-weighted graphs, given a formation pattern in terms of biases. A multi-strategy mutation differential evolution algorithm is introduced to solve the optimization problem. Simulation and real-world experiments are performed considering multi-robot systems composed of differential drive robots. Additionally, the experimental setup includes Turtlebot3® Waffle Pi robots and an OptiTrack® motion capture system for control purposes. The experimental results verify the effectiveness of the proposed approach.

Robust Collision Avoidance for Mobile Robots in the Presence of Moving Obstacles

This paper is concerned with the collision avoidance for mobile robots with uncertain dynamics in the presence of obstacles that can considerably change their velocities over time. To address this problem, we propose a robust collision-avoidance method based on the continuous-control-obstacles one. The proposed method uses an arbitrary-order overdamped low-pass filter to generate sufficiently smooth position commands for the robot and a high-order sliding mode differentiator to robustly estimate the obstacles’ maximum accelerations. Based on these estimates, we define a set of possible future positions for the obstacles according to how each one is changing its velocity to calculate a robust position command for the robot. The method has been numerically evaluated using a conventional quadcopter flying among moving obstacles and has been shown to be effective in providing collision avoidance and velocity constraints satisfaction.

Motion Saliency-Based Collision Avoidance for Mobile Robots in Dynamic Environments

Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this article, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method.

Solving Chance-Constrained Optimization Under Nonparametric Uncertainty Through Hilbert Space Embedding

In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation to formulate chance-constrained optimization as one of minimizing the distance between a desired distribution and the distribution of the constraint functions in the RKHS. We provide a systematic way of constructing the desired distribution based on the notion of scenario approximation. Furthermore, we use the kernel trick to show that the computational complexity of our reformulated optimization problem is comparable to solving a deterministic variant of the chance-constrained optimization. We validate our formulation on two important robotic applications: 1) reactive collision avoidance of mobile robots in uncertain dynamic environments and 2) inverse-dynamics-based path-tracking of manipulators under perception uncertainty. In both these applications, the underlying chance constraints are defined over nonlinear and nonconvex functions of uncertain parameters and possibly also decision variables. We also benchmark our formulation with the existing approaches in terms of sample complexity and the achieved optimal cost highlighting significant improvements in both these metrics.

A Formal Characterization of Black-Box System Safety Performance With Scenario Sampling

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this letter, we first present a novel safety evaluation criterion that seeks to characterize the actual operational domain within which the test subject would remain safe indefinitely with high probability. By formulating the black-box testing scenario as a dynamic system, we show that the presented problem is equivalent to finding a certain "almost" robustly forward invariant set for the given system. Second, for an arbitrary scenario testing strategy, we propose a scenario sampling algorithm that is provably asymptotically optimal in obtaining the safe invariant set with arbitrarily high accuracy. Moreover, as one considers different testing strategies (e.g., biased sampling of safety-critical cases), we show that the proposed algorithm still converges to the unbiased approximation of the safety characterization outcome if the scenario testing satisfies a certain condition. Finally, the effectiveness of the presented scenario sampling algorithms and various theoretical properties are demonstrated in a case study of the safety evaluation of a control barrier function-based mobile robot collision avoidance system.

Review of wheeled mobile robot collision avoidance under unknown environment.

Recently, the working scenes of the robot have been emerging as diversity and complexity with gradually mature of robotic control technology. The challenge of robot adaptability emerges, especially in complicated and unknown environments. Among the numerous researches on improving the adaptability of robots, aiming at avoiding collision between robot and external environment, obstacle avoidance has drawn much attention. Compared to the global circumvention requiring the environmental information that is known, the local obstacle avoidance is a promising method due to the environment is possibly dynamic and unknown. This study is aimed at making a review of research progress about local obstacle avoidance methods for wheeled mobile robots (WMRs) under complex unknown environment in the last 20 years. Sensor-based obstacle perception and identification is first introduced. Then, obstacle avoidance methods related to WMRs' motion control are reviewed, mainly including artificial potential field (APF)-based, population-involved meta heuristic-based, artificial neural network (ANN)-based, fuzzy logic (FL)-based and quadratic optimization-based, etc. Next, the relevant research on Unmanned Ground Vehicles (UGVs) is surveyed. Finally, conclusion and prospection are given. Appropriate obstacle avoidance methods should be chosen based on the specific requirements or criterion. For the moment, effective fusion of multiple obstacle avoidance methods is becoming a promising method.

Velocity Obstacle Based on Vertical Ellipse for Multi-Robot Collision Avoidance

Bounding volume based approaches in velocity obstacle (VO) provide a good solution for collision avoidance of mobile robots with uncertainty. However, the VO built with the bounding footprint always has over-constraining problems which may lead to conservative maneuvers of the mobile robots. Addressing this problem, a vertical ellipse based velocity obstacle (VEVO) collision avoidance method is proposed in this paper. The method mitigates the over-constraining situation by building the footprint probability ellipse whose minor axis is vertical to the direction of the obstacle to minimize the VO area. Based on VEVO, a DWA (Dynamic Window Approach) integrated method is proposed to provide a set of available velocities in speed selection. According to different collision avoidance objectives like collision safety, shortest time consumption and shortest trajectory length, a multi-objective velocity selecting strategy is proposed to provide optimal velocities for motion planning. Furthermore, a dynamic local path adjustment method is proposed to help robots react to the closest obstacle (dynamic or static) according to different collision safety requirements. We validate our methods in a simulated workspace with different numbers of robots going to their goal points. Experimental results show VEVO method could improve the collision avoidance performance in crowded multi-robot environment and robots could achieve their different objectives when suitable parameters are set in the velocity evaluation function. The proposed dynamic local path adjustment method only affects the trajectories in local areas and could ensure collision avoidance safety and performance at the same time.

Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity

Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions with a coarse model using RMPC. In many applications robustness is required for the short-term future, but in the long-term future, subject to major uncertainty and potential modeling difficulties, robust planning can lead to highly conservative solutions. We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions. This yields decreased computational effort due to a simple model for long-term predictions, and less conservative solutions, as robustness is only required for short-term predictions. The effectiveness of the method is shown in a mobile robot collision avoidance simulation.

Collision avoidance for mobile robots with limited sensing and limited information about moving obstacles

This paper addresses the problem of safely navigating a mobile robot with limited sensing capability and limited information about stationary and moving obstacles. We consider two sensing limitations: blind spots between sensors and limited sensing range. We study three notions of safety: (1) static safety, which ensures collision-freedom with respect to stationary obstacles, (2) passive safety, which ensures collision-freedom while the robot is moving, and (3) passive friendly safety, which ensures the robot leaves sufficient room for obstacles to avoid collisions. We present a runtime approach, based on the Simplex architecture, to ensure these safety properties. To obtain the switching logic for the Simplex architecture, we identify a set of constraints on the sensor readings whose satisfaction at time t guarantees that the robot will still be able to ensure the safety property at time \(t + {\varDelta } t\), regardless of how it navigates during that time interval. Here, \({\varDelta } t\) is the period with which the switching logic is executed and is bounded by a function of the maximum velocity and braking power of the robot and the range of the sensors. To the best of our knowledge, this work is the first that provides runtime assurance that an autonomous mobile robot with limited sensing can navigate safely with limited information about obstacles. The limited information about obstacles is used to derive an over-approximation of the set of nearby obstacle points.

Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling

Purpose This paper aims to investigate the collision avoidance problem for a mobile robot by constructing an artificial potential field (APF) based on geometrically modelling the obstacles with a new method named the obstacle envelope modelling (OEM). Design/methodology/approach The obstacles of arbitrary shapes are enveloped in OEM using the primitive, which is an ellipse in a two-dimensional plane or an ellipsoid in a three-dimensional space. As the surface details of obstacles are neglected elegantly in OEM, the workspace of a mobile robot is made simpler so as to increase the capability of APF in a clustered environment. Findings Further, a dipole is applied to the construction of APF produced by each obstacle, among which the positive pole pushes the robot away and the negative pole pulls the robot close. Originality/value As a whole, the dipole leads the robot to make a derivation around the obstacle smoothly, which greatly reduces the local minima and trajectory oscillations. Computer simulations are conducted to demonstrate the effectiveness of the proposed approach.

Collaborative Optimal Reciprocal Collision Avoidance for Mobile Robots

Collaborative Optimal Reciprocal Collision Avoidance for Mobile Robots

A behavior-based approach for collision avoidance of mobile robots in unknown and dynamic environments

This paper introduces a new behavior-based collision avoidance approach for mobile robot navigation in unknown and dynamic environments, which called Nearest Virtual-Target NVT. The NVT approach wa...

Collision Avoidance of Mobile Robot Based on Prediction of Human Movement According to Environments

Collision Avoidance of Mobile Robot Based on Prediction of Human Movement According to Environments

A behavior-based approach for collision avoidance of mobile robots in unknown and dynamic environments

This paper introduces a new behavior-based collision avoidance approach for mobile robot navigation in unknown and dynamic environments, which called Nearest Virtual-Target NVT. The NVT approach was developed based on a modeling-planning-reaction configuration. In modeling module, sensory information is integrated to construct a local model of environment which represents obstacles distribution and free obstacle areas in a part of robot's work space. The planning module uses the “actual-virtual target switching strategy” to compute obstacle free paths towards the target. The robot motion generation is handled by the reaction module. The reaction module applies a fuzzy controller to control the robot's rotational and translational velocities. The contribution of this approach is solving navigation difficulties presented in previous approaches for successful motion of the robot towards the target in troublesome scenarios such as narrow passages, very dense, cluttered and dynamic environments. Feasibility and effectiveness of the proposed approach are verified through simulation and real robot experiments. Eventually, advantages and limitations of this approach are discussed.

Mobile Robot Collision Avoidance in Human Environments

Collision avoidance is a fundamental requirement for mobile robots. Avoiding moving obstacles (also termed dynamic obstacles) with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. In this paper, both active and critical regions are used to deal with the uncertainty of human motion. A procedure is introduced to calculate the region sizes based on worst-case avoidance conditions. Next, a novel virtual force field-based mobile robot navigation algorithm (termed QVFF) is presented. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions for avoiding moving obstacles and its stability is proven using a piecewise continuous Lyapunov function. Simulation and experimental results are provided for a human walking towards the robot and blocking the path to a goal location. Next, the proposed algorithm is compared with five state-of-the-art navigation algorithms for an environment with one human walking with an unpredictable change in direction. Finally, avoidance results are presented for an environment containing three walking humans. The QVFF algorithm consistently generated collision-free paths to the goal.

1P1-C12 移動ロボットの衝突回避のための人間の移動予測アルゴリズム

1P1-C12 移動ロボットの衝突回避のための人間の移動予測アルゴリズム

Vision Based Target Tracking and Collision Avoidance for Mobile Robots

A real-time object tracking and collision avoidance method is presented for mobile robot navigation in indoors environments using stereo vision and a laser sensor. Stereo vision is used to identify the target and to calculate its relative distance from the mobile robot while laser based range measurements are utilized to avoid collision with surrounding objects. The target is tracked by its predetermined or dynamically defined color. The mobile robot's velocity is dynamically adjusted according to its distance from the target. Experimental results in indoor environments demonstrate the effectiveness of the method.

Extending Fuzzy Directional Relationship and Applying for Mobile Robot Collision Avoidance Behavior

Fuzzy directional relationship is extended from crisp spatial relationship and applied for many problems as image processing, scene description. This paper deals with fuzzy directional relationship and proposes an approach to extend fuzzy directional relation for robot navigation based on behavior. Fuzzy directional relation is used to model the unknown environment perceived by mobile robot. Collision avoidance behavior is built for mobile robot to avoid obstacles based on fuzzy logic controller whose inputs are fuzzy relationship and range to obstacle. The simulated results on graphic environment are showed to demonstrate our approach.

Improving collision avoidance for mobile robots in partially known environments: the beam curvature method

This paper presents a new approach to obstacle avoidance for mobile robots in cluttered and unknown or partially unknown environments. The method combines a new directional method, called beam method (BM), to improve the performance of a local obstacle avoidance approach called curvature velocity method (CVM). BM calculates the best one-step heading which is used by CVM to obtain the optimal linear and angular velocities. The resulting combined technique is called beam curvature method (BCM). Different experiments in populated and dynamic environments have proved to be very successful. The method is able to guide the robot safely and efficiently during long time periods. We present some of these results compared with other methods.