Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots

Chi, Wenzheng; Wang, Chaoqun; Wang, Jiankun; Meng, Max Q.-H.

doi:10.1109/tase.2018.2877963

Cited by 53 publications

(22 citation statements)

References 25 publications

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“…Several measures of navigation performance had been previously used to assess the performance of machine-learning-based methods for social navigation behaviours in autonomous robots [66][67][68][69][70][71][72]. However, these studies focused on algorithms that automatically plan a socially acceptable path without involvement of a human operator.…”

Section: Plos Onementioning

confidence: 99%

How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments

et al. 2020

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Telepresence robots allow users to be spatially and socially present in remote environments. Yet, it can be challenging to remotely operate telepresence robots, especially in dense environments such as academic conferences or workplaces. In this paper, we primarily focus on the effect that a speed control method, which automatically slows the telepresence robot down when getting closer to obstacles, has on user behaviors. In our first user study, participants drove the robot through a static obstacle course with narrow sections. Results indicate that the automatic speed control method significantly decreases the number of collisions. For the second study we designed a more naturalistic, conference-like experimental environment with tasks that require social interaction, and collected subjective responses from the participants when they were asked to navigate through the environment. While about half of the participants preferred automatic speed control because it allowed for smoother and safer navigation, others did not want to be influenced by an automatic mechanism. Overall, the results suggest that automatic speed control simplifies the user interface for telepresence robots in static dense environments, but should be considered as optionally available, especially in situations involving social interactions.

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Section: Plos Onementioning

confidence: 99%

How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments

et al. 2020

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“…As one of the key technologies of mobile robots, trajectory planning has recently attracted plenty of research. A series of trajectory planning schemes have been reported until now, such as the graph search-based method [ 2 , 3 ], interpolating curve planning method [ 4 , 5 ], sampling-based planning method [ 6 ], and numerical optimization method [ 7 ]. Among these methods, the interpolating curve planning method is a widely examined planning strategy due to its optimized performance and strong ability to handle external constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Zhang

Huang

Rong

et al. 2021

Sensors

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With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

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“…In a majority of practical scenarios, unmanned ground vehicles exhibit significantly less motion uncertainty compared to unmanned surface vehicles. Trajectory planning for unmanned ground vehicles is a well-studied problem and in the recent years, significant progress has been made in solving it using a wide variety of methods such as graph search, 6 stochastic tree search, 7,8 Markov decision processes (MDPs), and optimal control. 9,10 Graph search methods like state lattice search are popular global planning methods due to optimality guarantees and efficiency in large environments.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

Rajendran

Moscicki

Wampler

et al. 2020

International Journal of Advanced Robotic Systems

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We present a deliberative trajectory planning method to avoid collisions with traffic vessels. It also plans traversal across wavefields generated by these vessels and minimizes the risk of failure. Our method searches over a state-space consisting of pose and time. And, it produces collision-free and minimum-risk trajectory. It uses a lookup table to account for motion uncertainty and failure risk. We also present speed-up techniques to increase performance. Our wave-aware planner produces plans that (1) have shorter execution times and safer when compared to previously developed reactive planning schemes and (2) comply with user-defined wave-traversal constraints and Collision Regulations (COLREGs)

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Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots

Cited by 53 publications

References 25 publications

How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments

How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

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