A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Catherine, Maxime Sainte; Lucet, Éric

doi:10.1109/iros45743.2020.9341377

Cited by 8 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on Eq. ( 1), one can derive the nonholonomic constraint ṗx (t) sin(θ) − ṗy (t) cos(θ) = 0, indicating that the mobile robot cannot perform lateral movement [29]. However, forward maneuvers can be performed as one has ṗx (t) cos(θ)+ ṗy (t) sin(θ) = v(t).…”

Section: A Dynamic Model and System Constraints Of A Mobile Robotmentioning

confidence: 99%

See 1 more Smart Citation

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

Chai

Niu

Carrasco

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

This paper is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper-level motion planning layer and a lower-level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently-proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower-level, a deep reinforcement learning (DRL)-based collisionfree control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.

show abstract

Section: A Dynamic Model and System Constraints Of A Mobile Robotmentioning

confidence: 99%

“…where P i represents the sampling probability of the i th transition, α is distribution factor and p i stands for the priority of a set of transition data, formulated in (29).…”

Section: E Noisy Prioritized Experience Replay Algorithmmentioning

confidence: 99%

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

Chai

Niu

Carrasco

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…A similar approach (NH-ORCA) is presented in [8], where the radius of a nonholonomic robot is enlarged by a tracking error between the perfect holonomic motions and nonholonomic motions. Some recent efforts for nonholonomic collision avoidance considering RVO can be found in [9] and [10].…”

Section: Related Workmentioning

confidence: 99%

Reciprocal Collision Avoidance for Nonholonomic Mobile Robots

Wang

Chen

et al. 2018

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

View full text Add to dashboard Cite

We present a method for deadlock-free and collisionfree navigation in a multi-robot system with nonholonomic robots. The problem is solved by quadratic programming and is applicable to most wheeled mobile robots with linear kinematic constraints. We introduce masked velocity and Masked Cooperative Collision Avoidance (MCCA) algorithm to encourage a fully decentralized deadlock avoidance behavior. To verify the method, we provide a detailed implementation and introduce heading oscillation avoidance for differential-drive robots. To the best of our knowledge, it is the first method to give very promising and stable results for deadlock avoidance even in situations with a large number of robots and narrow passages.

show abstract

“…Lv et al ( 2019 ) cited the dense connection method to improve the Q-networks structure to solve the issue of robot drift by adopting the framework of a dense network. Sainte Catherine and Lucet ( 2020 ) combined with the improved Hybrid Reciprocating Speed Obstacle (HRVO) method of tracking error estimation, and adapting the speed obstacle paradigm to agents with dynamic constraints and unreliable velocity estimation. Yilmaz et al ( 2021 ) uses the fuzzy logic network to model dynamic uncertainty, and proposes a new definition of the error-like vector containing the pseudo-inverse of the Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

et al. 2022

View full text Add to dashboard Cite

Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm.

show abstract

A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Cited by 8 publications

References 17 publications

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

Reciprocal Collision Avoidance for Nonholonomic Mobile Robots

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

Contact Info

Product

Resources

About