Optimization-based methods for real-time generation of safe motions in mobile robots

Having robots operating in unstructured and dynamically changing environments is a challenging task that requires advanced motion generation approaches that are able to perform in real-time while maintaining the robot and environment safety. The progress in the field of numerical optimization, as well as the development of tailored algorithms, made Nonlinear Model Predictive Control (NMPC) an appealing candidate for real-time motion generation. By considering the robot model as prediction model and through appropriate constraints on the robot states and control inputs, NMPC can enforce safety to the resulting motion in a straightforward way. This thesis addresses the problem of real-time generation of safe motions for mobile robots and mobile manipulators. The different structure of the considered robots introduces different safety risks during the robot motion and so the motion generation problem for each robot is addressed in separate parts of this thesis. In the first part, the problem of motion generation for mobile robots navigating in environments populated by static and/or moving obstacles is considered. For the generation of the desired motion, real-time NMPC is used. We argue that, in order to tackle the risk of collision with the environment, traditional distance-based approaches are incapable of maintaining the robot safety when the NMPC uses relatively short prediction horizons. Instead, we propose two NMPC approaches that employ two alternative collision avoidance constraints. The first proposed NMPC approach is applied to a scenario of safe robot navigation in a human crowd. The NMPC serves as a motion generation module in a safe motion generation framework, complete with a crowd prediction module. The considered collision avoidance constraint is built upon an appropriate Control Barrier Function (CBF). The second NMPC approach is applied to a scenario of robot navigation among moving obstacles, where the dynamics of the considered robot are significant. The proposed collision avoidance constraint is built upon the notion of avoidable collision state, which considers not only the robot-obstacle distance but also their velocity as well as the robot actuation capabilities. The simulation results indicate that both methods are effective and able to maintain the robot safety even in cases where their purely distance-based counterparts fail. The second part of the thesis addresses the problem of safe motion generation for mobile manipulators, called to execute tasks that may require aggressive motions. Here, in addition to the risk of collision with its environment, the robot, consisting of multiple articulated bodies, is also susceptible to self-collisions. Moreover, fast motions can always result to loss of balance. To solve the problem, we propose a real-time NMPC scheme that uses the robot full dynamics, in order to enforce kinodynamic feasibility, while it also considers appropriate collision and self-collision avoidance constraints. To maintain the robot balance we enforce a constraint that restricts the feasible set of robot motions to those generating non-negative moments around the edges of the support polygon. This balance constraint, inherently nonlinear, is linearized using the NMPC solution of the previous iteration. In this way, we facilitate the solution of the NMPC in real-time, without compromising the robot safety. Although the proposed NMPC is effective when applied to MM with low degrees of freedom, when the robot becomes more complex the use of its full dynamic model as a prediction model in an NMPC can lead to unacceptably large computational times that are not compatible with the real-time requirement. However, the use of a simplified model of the robot in an NMPC can compromise the robot safety. For this reason, we propose an optimization-based controller equipped with balance constraints as well as CBF-based collision avoidance constraints. The proposed controller can serve as an intermediate between a motion generation module that does not consider the robot full dynamics and the robot itself in order to ensure that the resulting motion will be at least safe. Simulation results indicate the effectiveness of the proposed method

Autonomous robotic wheelchair with collision-avoidance navigation

The objective of this research is to demonstrate a robotic wheelchair moving in an unknown environment with collision-avoidance navigation. A real-time path-planning algorithm was implemented by detecting the range to obstacles and by tracking specific light sources used as beacons. Infrared sensors were used for range sensing, and light-sensitive resistors were used to track the lights. To optimize the motion trajectory, it was necessary to modify the original motor controllers of the electrical wheelchair so that it could turn in a minimum turning radius of 28.75 cm around its middle point of axle. Then, with these kinematics, the real-time path planning algorithm of the robotic wheelchair is simplified. In combination with the newly developed wireless Internet-connection capability, the robotic wheelchair will be able to navigate in an unknown environment. The experimental results presented in this thesis include the performance of the control system, the motion trajectory of the two driving wheels turning in a minimum radius, and the motion trajectory of the real-time path-planning in a real-life testing environment. These experimental results verified that the robotic wheelchair could move successfully in an unknown environment with collision-avoidance navigation

A Furcated Visual Collision Avoidance System for an Autonomous Micro Robot

This paper proposes a secondary reactive collision avoidance system for micro class of robots based on a novel approach known as the Furcated Luminance-Difference Processing (FLDP) inspired by the Lobula Giant Movement Detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges; obstacle proximity & direction estimation, and operation in GPS-denied environment with irregular lighting. Additionally, it has proven effective in detecting edges independent of background color, size, and contour. The FLDP executes a series of image enhancement and edge detection algorithms to estimate collision threat-level which further determines whether or not the robot’s field of view must be dissected where each section’s response is compared against the others to generate a simple collision-free maneuver. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios validating the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision, moving at 1.2 ms¯¹ with a successful avoidance rate of 90% processing at 120 Hz on a simple single core microcontroller, sufficient to conclude the system’s feasibility for real-time real-world applications that possess fail-safe collision avoidance system

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0

Towards Safer Obstacle Avoidance for Continuum-Style Manipulator in Dynamic Environments

The flexibility and dexterity of continuum manipulators in comparison with rigid-link counterparts have become main features behind their recent popularity. Despite of that, the problem of navigation and motion planning for continuum manipulators turns out to be demanding tasks due to the complexity of their flexible structure modelling which in turns complicates the pose estimation. In this paper, we present a real-time obstacle avoidance algorithm for tendondriven continuum-style manipulator in dynamic environments. The algorithm is equipped with a non-linear observer based on an Extended Kalman Filter to estimate the pose of every point along the manipulator’s body. A local observability analysis for the kinematic model of the manipulator is also presented. The overall algorithm works well for a model of a single-segment continuum manipulator in a real-time simulation environment with moving obstacles in the workspace of manipulators, able to avoid the whole body of manipulators from collision

Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments—a case study

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs

Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study.

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs