Showing papers on "Collision avoidance published in 2020"

Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance

Abstract: In this paper, a cooperative time-varying formation maneuvering problem with connectivity preservation and collision avoidance is investigated for a fleet of autonomous surface vehicles (ASVs) with position–heading measurements. Each vehicle is subject to unknown kinetics induced by internal model uncertainty and external disturbances. At first, a nonlinear state observer is used to recover the unmeasured linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based cooperative time-varying formation maneuvering control laws are designed based on artificial potential functions, nonlinear tracking differentiators, and a backstepping technique. The stability of closed-loop distributed formation control system is analyzed based on input-to-state stability and cascade stability. The salient features of the proposed method are as follows. First, cooperative time-varying formation maneuvering with the capability of connectivity preservation and collision avoidance can be achieved in the absence of velocity measurements. Second, the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control. Third, the uncertainty and disturbance are actively rejected in the presence of position–heading measurements. Simulation results are given to substantiate the proposed output feedback control method for cooperative time-varying formation maneuvering of ASVs with connectivity preservation and collision avoidance.

Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios

Abstract: Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other r...

Unmanned Aerial Vehicles (UAVs) : Collision Avoidance Systems and Approaches

Abstract: Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessit ...

Longitudinal Collision Avoidance and Lateral Stability Adaptive Control System Based on MPC of Autonomous Vehicles

Abstract: The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCACS) based on the model predictive control (MPC). First, the proposed LSCACS decides which control mode to be implemented based on vehicle dynamics states, including a normal driving mode, a full auto brake mode, and a brake and stability mode. The MPC is used in the upper controller to calculate the desired deceleration and additional yaw moment. The lower controller calculates the desired tire forces of four wheels and realizes them by certain wheel cylinder hydraulic pressures. The LSCACS is validated by hardware-in-the-loop (HIL) tests, and the results show LSCACS’s effectiveness and great performance of the collision avoidance and lateral stability.

Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning

Abstract: We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50% of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90% success rate with 30 palm-sized quadrotor agents in a 18 $\text{m}^3$ arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.

Convergent Multiagent Formation Control With Collision Avoidance

Abstract: A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions Distributed control rules are also defined to confine the motion space of each agent for collision avoidance It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm

Formation control and collision avoidance for multi-UAV systems based on Voronoi partition

Abstract: This paper deals with the formation control problem of multiple unmanned aerial vehicles (UAVs) with collision avoidance. A distributed formation control and collision avoidance method is proposed based on Voronoi partition and conventional artificial potential field. The collision avoidance is achieved by partitioning the whole space into non-overlapping regions based on Voronoi partition theory, which is taken as the task region to confine the movement of each UAV. The general motion control law is designed based on the conventional artificial potential field. As this often leads to local optimum when two UAVs are going to collide with each other and they may stay still where the repulsive force is adversely equivalent to the attractive force. To address this problem, the destination switch scheme is further proposed to let UAVs switch destinations when they reach the local equilibrium. Finally, the effectiveness of proposed formation control algorithm is validated by simulations and experiments.

Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles

Abstract: This article proposes a Novel Nonlinear Model Predictive Control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV). The proposed NMPC formulation allows for a fully p ...

Multiobjective Optimization of Lane-Changing Strategy for Intelligent Vehicles in Complex Driving Environments

Abstract: This paper describes an optimal lane-changing strategy for intelligent vehicles under the constraints of collision avoidance in complex driving environments. The key technique is optimization in a collision-free lane-changing trajectory cluster. To achieve this cluster, a tuning factor is first derived by optimizing a cubic polynomial. Then, a feasible trajectory cluster is generated by adjusting the tuning factor in a stable handling envelope defined from vehicle dynamics limits. Furthermore, considering the motions of surrounding vehicles, a collision avoidance algorithm is employed in the feasible cluster to select the collision-free trajectory cluster. To extract the optimal trajectory from this cluster, the TOPSIS algorithm is utilized to solve a multiobjective optimization problem that is subject to lane change performance indices, i.e., trajectory following, comfort, lateral slip and lane-changing efficiency. In this way, the collision risk is eliminated, and the lane change performance is improved. Simulation results show that the strategy is able to plan suitable lane-changing trajectories while avoiding collisions in complex environments.

A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing

Abstract: Unlike autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs) have a higher dimensional configuration space, which makes the motion planning of multi-UAVs a challenging task. In addition, uncertainties and noises are more significant in UAV scenarios, which increases the difficulty of autonomous navigation for multi-UAV. In this letter, we proposed a two-stage reinforcement learning (RL) based multi-UAV collision avoidance approach without explicitly modeling the uncertainty and noise in the environment. Our goal is to train a policy to plan a collision-free trajectory by leveraging local noisy observations. However, the reinforcement learned collision avoidance policies usually suffer from high variance and low reproducibility, because unlike supervised learning, RL does not have a fixed training set with ground-truth labels. To address these issues, we introduced a two-stage training method for RL based collision avoidance. For the first stage, we optimize the policy using a supervised training method with a loss function that encourages the agent to follow the well-known reciprocal collision avoidance strategy. For the second stage, we use policy gradient to refine the policy. We validate our policy in a variety of simulated scenarios, and the extensive numerical simulations demonstrate that our policy can generate time-efficient and collision-free paths under imperfect sensing, and can well handle noisy local observations with unknown noise levels.

Reinforcement Learning-based Collision Avoidance and Optimal Trajectory Planning in UAV Communication Networks

Abstract: In this paper, we propose a reinforcement learning approach of collision avoidance and investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks Specifically, each UAV takes charge of delivering objects in the forward path and collecting data from heterogeneous ground IoT devices in the backward path We adopt reinforcement learning for assisting UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance In addition, for each UAV, we use optimization theory to find out a shortest backward path that assures data collection from all associated IoT devices To obtain an optimal visiting order for IoT devices, we formulate and solve a no-return traveling salesman problem Given a visiting order, we formulate and solve a sequence of convex optimization problems to obtain line segments of an optimal backward path for heterogeneous ground IoT devices We use analytical results and simulation results to justify the usage of the proposed approach Simulation results show that the proposed approach is superior to a number of alternative approaches

A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

Abstract: Lane change maneuvers, are important contributors to road traffic accidents on highway. In this paper, we propose a new safe lane change trajectory model and collision avoidance control method for Society of Automotive Engineers (SAE)-level 2 automatic driving vehicles. First, a Gaussian distribution is used to describe the new trajectory model that uses pure steering and combined braking. According to regional and progressive states, a new safe lane change meaning is defined. Second, we design a new four-level automatic driving mode and an effective decision mechanism that considers safety and ergonomics. Moreover, a new trajectory tracking controller combined with a decision mechanism was designed using feedback linearization, verified using typical lane-change scenarios. Finally, based on a physical simulation platform, PreScan, the hardware-in-the-loop simulation result demonstrate the feasibility and effectiveness of our method. This paper provides a valuable reference on an expert and intelligent system methodology for automatic driving vehicles, which will be helpful for improving highway traffic safety and efficiency.

Tractor-Trailer Vehicle Trajectory Planning in Narrow Environments With a Progressively Constrained Optimal Control Approach

Abstract: Trajectory planning for a tractor-trailer vehicle is challenging due to the non-convex collision avoidance constraints and the unstable kinematics. This article aims to plan precise and optimal trajectory for a car-like tractor that tows standard or general N-trailers in an environment with static obstacles. This trajectory planning scheme can be formulated as a unified optimal control problem, but numerically solving that problem is difficult. Two efforts are made to address the difficulty. First, an extended hybrid A* search algorithm is proposed to provide an initial guess for the numerical optimization. Second, the numerical optimization process is eased through adopting a progressively constrained optimization strategy, which is about sequentially handling the kinematics, collision avoidance constraints, and boundary conditions. Particularly in dealing with the collision avoidance constraints, an adaptively homotopic warm-starting algorithm is proposed, which defines a sequence of subproblems with the obstacle areas adaptively increase towards their nominal scales. Through solving these subproblems in a sequence, the whole collision avoidance difficulties are dispersed and gradually tackled. Comparative simulations are conducted to validate the efficacy of the proposed trajectory planner.

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

Abstract: Collision avoidance for multi-robot systems is a difficult challenge under uncertainty, non-determinism, and lack of complete information. This paper aims to propose a collision avoidance framework that accounts for both measurement uncertainty and bounded motion uncertainty. In particular, we propose Probabilistic Safety Barrier Certificates (PrSBC) using Control Barrier Functions to define the space of possible control actions that are probabilistically safe. The framework entails minimally modifying an existing unconstrained controller to determine a safe controller via a quadratic program constrained to the chance-constrained safety set. The key advantage of the approach is that no assumptions about the form of uncertainty are required other than finite support, also enabling worst-case guarantees. We demonstrate effectiveness of the approach through experiments on a realistic simulation environment.

DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

Abstract: We present DCAD, a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal Collision Avoidance (ORCA) and utilizes a flatness-based Model Predictive Control (MPC) to generate local collision-free trajectories for each quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor and subsequently use this linearized model in our MPC framework. Our approach tends to compute safe trajectories that avoid quadrotors from entering each other's downwash regions during close proximity maneuvers. In addition, we account for the uncertainty in the position and velocity sensor data using Kalman filter. We evaluate the performance of our algorithm with other state-of-the-art decentralized methods and demonstrate its superior performance in terms of smoothness of generated trajectories and lower probability of collision during high-velocity maneuvers.

Distributed Formation Navigation of Constrained Second-Order Multiagent Systems With Collision Avoidance and Connectivity Maintenance.

Abstract: In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints Both collision avoidance and connectivity maintenance of the network are considered in the controller design A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs A nonsmooth analysis is employed to prove the global convergence of the controller Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived The barrier function-based collision-avoidance conditions are also developed All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance Simulation results demonstrate the effectiveness of the proposed control strategy

Learning-Based Proxy Collision Detection for Robot Motion Planning Applications

Abstract: This article demonstrates that collision detection-intensive applications such as robotic motion planning may be accelerated by performing collision checks with a machine learning model. We propose Fastron, a learning-based algorithm, to model a robot's configuration space to be used as a proxy collision detector in place of standard geometric collision checkers. We demonstrate that leveraging the proxy collision detector results in up to an order of magnitude faster performance in robot simulation and planning than state-of-the-art collision detection libraries. Our results show that Fastron learns a model more than 100 times faster than a competing C-space modeling approach, while also providing theoretical guarantees of learning convergence. Using the open motion planning libraries (OMPLs), we were able to generate initial motion plans across all experiments with varying robot and environment complexities and workspace obstacle locations. With Fastron, we can repeatedly generate new motion plans at a 56 Hz rate, showing its application toward autonomous surgical assistance task in shared environments with human-controlled manipulators. All performance gains were achieved despite using only CPU-based calculations, suggesting further computational gains with a GPU approach that can parallelize tensor algebra. Code is available online. 1