Ensure Obstacle Avoidance Research Articles

Safety-Critical Fixed-Time Formation Control of Quadrotor UAVs with Disturbance Based on Robust Control Barrier Functions

This paper focuses on the safety-critical fixed-time formation control of quadrotor UAVs with disturbance and obstacle collision risk. The control scheme is organized in a distributed manner, with the leader’s position and velocity being estimated simultaneously by a fixed-time distributed observer. Meanwhile, a disturbance observer that combines fixed-time control theory and sliding mode control is designed to estimate the external disturbance. Based on these techniques, we design a nominal control law to drive UAVs to track the desired formation in a fixed time. Regarding obstacle avoidance, we first construct safety constraints using control barrier functions (CBFs). Then, obstacle avoidance can be achieved by solving an optimization problem with these safety constraints, thus minimally affecting tracking performance. The main contributions of this process are twofold. First, an exponential CBF is provided to deal with the UAV model with a high relative degree. Moreover, a robust exponential CBF is designed for UAVs with disturbance, which provides robust safety constraints to ensure obstacle avoidance despite disturbance. Finally, simulation results show the validity of the proposed method.

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

Multi-Robot Trajectory Planning With Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environment

This article presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive optimization problem, where a novel safe corridor is generated online to ensure obstacle avoidance in trajectory planning. A dynamic-priority mechanism is combined with the right-hand rule to handle potential deadlocks that are much harder to resolve due to static obstacles. Comparisons with other state-of-the-art results are conducted to validate the improved safety and success rate. Additional hardware experiments are carried out with up to eight nano-quadrotors in various cluttered scenarios.

Collision-free control of a nano satellite in the vicinity of China Space Station using Lorentz augmented composite artificial potential field

This study addresses the problem of trajectory control for a nano satellite flying very closely near China Space Station (CSS) with obstacle avoidance. The control law is designed based on a newly developed composite artificial potential field (APF) model combined with its induced artificial Lorentz force. The composite APF model could precisely and efficiently compute the total potential filed contributed from each element that constitutes the whole body of CSS. Those elements are modeled as the common geometric entities such as cuboids and capsule bodies to facilitate distance calculation, so that their independent APFs can be easily modelled in analytical form. In this way, the nano satellite under the composite APF-based control could fly anywhere outside CSS, which cannot be easily achieved by other APF models. To solve the local minima problem which often occur in classical APF-based methods, it is further proposed that an APF-induced artificial Lorentz force is added to the original control law. The stability of the closed-loop system with the control law is proved using Lyapunov theory. Finally, some numerical examples are designed to show the effectiveness of the proposed control law. Since the composite APF and its induced artificial Lorentz force only relies on CSS’s partial structure near the nano satellite, the method is suitable in the practical missions where the obstacle’s information cannot be fully and accurately known. The method here can be applied in the practical missions such as on-orbit ultra-close photographing of CSS by nano satellites in future. • An APF modeling framework is proposed for obstacle made up of fundamental solids. • An analytical APF is constructed for CSS, which suits its simplified shape. • The nano satellite could fly anywhere outside CSS, while ensuring obstacle avoidance. • An APF-induced artificial Lorentz force is designed to avoid local minima effectively. • The control law with Lorentz augmented APF only relies on local information of CSS.

Safety-Critical Trajectory Generation and Tracking Control of Autonomous Underwater Vehicles

Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV's actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.

Barrier-Certified Distributed Model Predictive Control of Under-Actuated Autonomous Surface Vehicles via Neurodynamic Optimization

This article addresses the distributed formation control of multiple under-actuated autonomous surface vehicles (ASVs) in a receding-horizon setting. The ASVs are subject to physical constraints, in addition to stationary and moving obstacles. A barrier-certified distributed model predictive control method is proposed with the capability of avoiding collision with stationary and moving obstacles and neighboring ASVs. Specifically, a data-driven neural predictor is used to learn unknown functions in ASV kinetics. A nominal distributed receding-horizon position control law is developed based on the learned unknown function to achieve the desired formation within physical constraints. To ensure the safety requirement, a barrier-certified control law is designed based on control barrier functions to generate the signals of optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed based on the data-driven neural predictor to track the desired heading signals. Constrained quadratic programming problems are formulated based on barrier functions for barrier-certified distributed formation control and solved via neurodynamic optimization using one-layer recurrent neural networks. Thus, the proposed control method is able to ensure obstacle avoidance in the formation control of multiple ASVs in the presence of stationary and moving obstacles. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified distributed model predictive control method for ASV formation.

Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators

In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.

Robot Navigation in Complex Workspaces Using Conformal Navigation Transformations

Navigation functions provide both path and motion planning, which can be used to ensure obstacle avoidance and convergence in the sphere world. When dealing with complex and realistic scenarios, constructing a transformation to the sphere world is essential and, at the same time, challenging. This work proposes a novel transformation termed the conformal navigation transformation to achieve collision-free navigation of a robot in a workspace populated with obstacles of arbitrary shape. The properties of the conformal navigation transformation, including uniqueness, invariance of navigation properties, and no angular deformation, are investigated, which contribute to the solution of the robot navigation problem in complex environments. Based on navigation functions and the proposed transformations, feedback controllers are derived for the automatic guidance and motion control of kinematic and dynamic mobile robots. Moreover, an iterative method is proposed to construct the conformal navigation transformation in a multiply-connected workspace, which transforms the multiply-connected problem into multiple simply connected problems to achieve fast convergence. In addition to the analytic guarantees, simulation studies verify the effectiveness of the proposed methodology in workspaces with non-trivial obstacles.

Towards Safe Exploration for Autonomous Vehicles using Dual Model Predictive Control

Collision-free control and planning for autonomous vehicles in an only partially known environment is challenging. Often this problem is tackled passively - without active exploration, i.e., planning and control are performed based on the information obtained during operation without explicitly considering that reducing the environment uncertainty by active exploration can improve the overall performance, e.g., reduce the time needed to achieve the goal. We propose a moving horizon planning approach combined with a dual-mode predictive control formulation that actively explores the environment to obtain improved obstacle information. Given a path by the moving horizon planner, the dual-mode predictive tracking controller balances two objectives: fast and safe goal achievement, taking into account potential improvements due to active exploration - learning - of the environment. Adding an excitation term to the objective function incentivizes the system to explore the environment to enhance the overall objective and reduce uncertainty. The exploration-exploitation formulation enhances the overall performance while ensuring obstacle avoidance in combination with the moving horizon planning strategy. An example of an autonomous ground vehicle operating in a cluttered, only partially known environment demonstrates the approach's efficacy.

Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

We propose a distributed control algorithm for a multi-agent network whose agents deploy over a cluttered region in accordance with a time-varying coverage density function while avoiding collisions with all obstacles they encounter. Our algorithm is built on a two-level characterization of the network. The first level treats the multi-agent network as a whole based on the distribution of the locations of its agents over the spatial domain. In the second level, the network is described in terms of the individual positions of its agents. The aim of the multi-agent network is to attain a spatial distribution that resembles that of a reference coverage density function (high-level problem) by means of local (microscopic) interactions of its agents (low-level problem). In addition, as the agents deploy, they must avoid collisions with all the obstacles in the region at all times. Our approach utilizes a modified version of Voronoi tessellations which are comprised of what we refer to as Obstacle-Aware Voronoi Cells (OAVC) in order to enable coverage control while ensuring obstacle avoidance. We consider two control problems. The first problem which we refer to as the high-level coverage control problem corresponds to an interpolation problem in the class of Gaussian mixtures (no collision avoidance requirement), which we solve analytically. The second problem which we refer to as the low-level coverage control problem corresponds to a distributed control problem (collision avoidance requirement is now enforced at all times) which is solved by utilizing Lloyd's algorithm together with the modified Voronoi tessellation (OAVC) and a time-varying coverage density function which corresponds to the solution of the high-level coverage control problem. Finally, simulation results for coverage in a cluttered environment are provided to demonstrate the efficacy of the proposed approach.

A Hybrid Joint/Cartesian DMP-Based Approach for Obstacle Avoidance of Anthropomorphic Assistive Robots

Anthropomorphic criteria are widely adopted for developing socially interactive robots since they can improve human capability to interpret and predict robot motion, with an impact on robot acceptability and human–robot interaction safety. Learning by demonstration approaches based on dynamic movement primitives are a suitable solution for planning the robot motion in human-like fashion and endow robots with generalization capabilities and robustness against perturbation. Objective of this work is to propose a new formulation of the learning by demonstration approach based on dynamic movement primitives (DMPs), called hybrid joint/Cartesian DMPs, for redundant robots with the twofold purpose of avoiding obstacles on the path and obtaining anthropomorphic motion in the joint as well as the task space. The proposed approach, suitable for assistive purposes, was experimentally validated on the anthropomorphic robot arm Kuka Light Weight Robot 4+. Trajectories recorded by an optoelectronic system (i.e. the BTS Smart D) from human demonstrators performing point-to-point reaching and pouring tasks were adopted to teach the robot how to move. A comparative analysis with current methods of learning by demonstration in the literature, i.e. Cartesian DMP and inverse kinematics, was performed. Performance indicators were introduced to (1) assess the robot accuracy of the motion performing, (2) evaluate the level of anthropomorphism of the computed motion, (3) measure the success rate in obstacle avoidance. Moreover, questionnaires were administered to ten human subjects who observed robot motion. The obtained results demonstrated that the proposed approach guarantees anthropomorphic motion of the robot both in the joint and in the task spaces ensuring obstacle avoidance along the robot kinematic chain. In particular, the anthropomorphic robot, while operating with the proposed method, exhibits behaviours in the joint space that are similar to the one recorded during the demonstration and has a level of anthropomorphism rather higher than the one obtained with Cartesian DMP and inverse kinematics (a configuration of the robot joints within the physiological limits is always ensured and the maximum convex hull created between the human and the robot arm is 0.0085 m\(^3\)). In presence of obstacles, an acceptable level of the Cartesian accuracy (\(Position~err<0.005\) m and \(Orientation~err<0.02\) rad) is achieved. The obstacles are avoided with a 100% of success. The questionnaire results showed that the users feel more comfortable and less nervous to interact with a robot that moves in human-like manner.

Sliding surface-based obstacle avoidance for second order multi-agent systems

This paper studies formation keeping along with obstacle avoidance for second-order multi-agent systems. First, the flexible virtual structure (FVS) approach, used to model the communication topology between the agents of the formation, is recalled and relationship with graph theory-based communication exchange to achieve consensus is established. Second, using the regulation function (RF) which permits to change the behaviour of the system's solution without affecting convergence, obstacle avoidance is investigated for one agent (leader/co-leader) of the formation. Particularly, RF was used to ensure obstacle avoidance for first order agents and is extended in this work for second order agents. Relationship between the dynamic agent and a sliding surface with first order kinematics is established and condition on the sliding surface parameter is developed to ensure obstacle avoidance. Also, new shapes of the obstacle are considered namely square and rhombus. Finally, in order to perform a coordinated obstacle avoidance of a multi-agent system when only a subset of agents namely co-leaders, selected from the boarder, has obstacle information, one defines control laws which permit to control motions of the remaining formation agents.

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

SUMMARYThis paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.

Clothoids Composition Method for Smooth Path Generation of Car-Like Vehicle Navigation

This paper addresses a continuous curvature path generation problem for car-like vehicle navigation. The continuous curvature path is generated by multiple clothoids composition and parametric adjustment. According to the geometric conditions of the given initial and final configurations, the path generation problem is classified into two cases and then, each problem is solved by by appropriate proposed algorithm. The solution is obtained by iterative procedure subject to geometric constraint as well as solution constraints. For computational efficiency and fast convergence in the proposed algorithms, a minimax sharpness constraint is proposed as the solution constraint by minimizing the maximum sharpness of the feasible solutions. After the generation of the proposed path, the resultant curvature/sharpness diagram provides a useful information about its orientation and curvature continuity along the travel length. The proposed path planning strategy, permits us to obtain online, smooth and safe path between two defined configurations while ensuring high passengers comfort (continuous curvature and transition between the different composed clothoids). The algorithmic proposals have been applied to generate continuous curvature for two cases. The first correspond to local path planning for ensuring obstacle avoidance or lane change. The second application corresponds to global path smoothing. The resultant global path path is tested on the Lyapunov-based control scheme and showed improved performance on its steering work (reduction of 41.0% than the driving based on the raw data), which permits us therefore to validate the effectiveness of the obtained global path for car-like vehicles path following.

A New Objective Function for Obstacle Avoidance by Redundant Service Robot Arms

The performance of task-space tracking control of kinematically redundant robots regulating self-motion to ensure obstacle avoidance is studied and discussed. As the sub-task objective, the links of the kinematically redundant assistive robot should avoid any collisions with the patient that is being assisted. The shortcomings of the obstacle avoidance algorithms are discussed and a new obstacle avoidance algorithm is proposed. The performance of the proposed algorithm is validated with tests that were carried out using the virtual model of a seven degrees-of-freedom robot arm. The test results indicate that the developed controller for the robot manipulator is successful in both accomplishing the main-task and the sub-task objectives.

Cooperative Visibility Maintenance for Leader–Follower Formations in Obstacle Environments

Vision-based formation control of multiple agents, such as mobile robots or fully autonomous cars, has recently received great interest due to its application in robotic networks and automated highways. This paper addresses the cooperative motion coordination of leader-follower formations of nonholonomic mobile robots, under visibility and communication constraints in known polygonal obstacle environments. We initially consider the case of N = 2 agents moving in L-F fashion and propose a feedback control strategy under which L ensures obstacle avoidance for both robots, while F ensures visibility maintenance with L and intervehicle collision avoidance. The derived algorithms are based on set-theoretic methods to guarantee visibility maintenance, dipolar vector fields to maintain the formation shape, and the consideration of the formation as a tractor-trailer system to ensure obstacle avoidance. We furthermore show how the coordination and control design extends to the case of N > 2 agents, and provide simulation results, which demonstrate the efficacy of the control solutions. The proposed algorithms do not require information exchange among robots, but are instead based on information locally available to each agent. In this way, the desired tasks are executed and achieved in a decentralized manner, with each robot taking care of converging to a desired configuration, while maintaining visibility with its target.

Potential-based modeling of three-dimensional workspace for obstacle avoidance

A potential-based model of three-dimensional workspace is proposed for ensuring obstacle avoidance in path planning. It is assumed that the workspace boundary is uniformly distributed with generalized charges. The potential due to a point charge is inversely proportional to the distance to the power of an integer, the order of the potential function. It is shown that such potential functions and their gradients due to polyhedral surfaces can be derived analytically, and thus can facilitate efficient collision avoidance. Intuitively, the potential fields and their effects on object paths should be spatially continuous and smooth. The continuity and differentiability properties of a particular potential function are investigated. In theory, by minimizing the repulsion between object and obstacles, the approach completely eliminates the possibility of a collision between them if the dynamics of the moving object is ignored.