A Smooth and Safe Path Planning for an Active Lower Limb Exoskeleton

Nascimento, Luís; Barrios-Aranibar, Dennis; Alsina, Pablo Javier; Santos, Vitor Gaboardi dos; Fernandes, Daniel H. S.; Pereira, Diego S.

doi:10.1007/s10846-019-01134-7

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…However, for practical experiments, this representation is computationally unfeasible. In this way, new bubbles can be easily computed using information from the workspace without needing the C-obstacle computation, as shown in [ 30 ]. Besides, it also allows us to compute bubbles for different robots.…”

Section: Resultsmentioning

confidence: 99%

“…Notice that the paths are not smooth, which can be a problem when considering a practical application. However, it is possible to apply optimization techniques to smooth the paths obtained from the probabilistic foam methods, as shown in [ 30 ], ensuring both safe and smooth paths.…”

Section: Resultsmentioning

confidence: 99%

“…A set of bubbles propagate through the free space from the initial configuration towards the goal configuration in a tree structure called probabilistic foam. The resulting path for this approach is extracted from a sequence of overlapped bubbles, called the rosary, which bounds the path and guarantees safe motions, also providing safety constraints for adjustments, such as path smoothing [ 30 ]. The rosary is illustrated in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Nascimento

Barrios-Aranibar

Santos

et al. 2021

Sensors

Self Cite

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The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Nascimento

Barrios-Aranibar

Santos

et al. 2021

Sensors

Self Cite

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“…The method Goal-Biased Probabilistic Foam (Nascimento et al, 2018b) is a global sampling-based path planner ideal for robots that need to perform safe motion. The GBPF is a variant of the Probabilistic Foam Method (Nascimento et al, 2018a(Nascimento et al, , 2020 where bubbles are expanded in the free configuration space and propagate with a strategy inspired on the expanding method of the search tree from the RRT-GoalBias algorithm Lavalle et al (2000), a variant of the classic Rapidly-Exploring Random Tree (RRT) LaValle (1998). The method GBPF converges to the goal configuration faster than the original PFM and provides paths with high clearance, differently from RRT.…”

Section: Goal-biased Probabilistic Foammentioning

confidence: 99%

“…Therefore, this feature can be used to perform path adjustments and, consequently, smooth the resulted path. Nascimento et al (2020) present an optimization approach to smooth the path obtained by path planners based on the probabilistic foam. We use the same approach in this work to make the path smoother.…”

Section: Path Smoothingmentioning

confidence: 99%

3D Path Planning Based on Probabilistic Foam for UAV Indoor Applications

Nascimento

Santos

Pereira

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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The recent growth of the Unmanned Aerial Vehicle (UAV) industry stimulated research development to solve different problems, such as inspection in hard-to-reach places, mapping, and search and rescue. Frequently, these applications require navigation in indoor environments, so searching for safe paths for the UAVs to fly is essential for mission accomplishment. In this context, we propose in this paper the employment of the Goal-Biased Probabilistic Foam (GBPF) method to perform the path planning for a UAV in indoor missions. The GBPF method ensures a fast search of a path with a safe region provided by a structure called bubble that will be modeled for the UAV. The bubble represents a free-space region that allows safe UAV maneuvers, decreasing the probability of obstacles collision. To validate the proposed methodology, we used the Virtual Robot Experimentation Platform (V-REP) to simulate an UAV flight in an indoor environment using the path generated by the GBPF.

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Optimization of Joint Space Trajectories for Assistive Lower Limb Exoskeleton Robots: Real-Time and Flexible Gait Patterns

Mohamad,

Ozgoli,

Kazemi

2024

J Intell Robot Syst

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This research focuses on designing a real-time, flexible gait planner for lower limb exoskeleton robots to assist patients with lower limb disabilities. Given the dynamic nature of gait parameters, which vary according to ground conditions and user intent, the challenge lies in developing a gait planner capable of adapting to these changes in real-time. To avoid planning complications in the cartesian space and to comply with the speed constraints of joint motors, this paper proposes planning in joint space. Furthermore, the approach also considers the maximum speed capabilities of the joint motors, aiming to generate an executable gait pattern and simultaneously enhance the robot’s walking speed by determining the minimum time required for implementation. The introduced gait planner optimizes joint trajectories for minimal angular acceleration. To provide flexibility, generalized boundary conditions suitable for different scenarios are defined. The effectiveness of the proposed planner is validated through comprehensive performance analysis, simulations, and successful implementation trials on the Exoped® robot in various scenarios.

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A Smooth and Safe Path Planning for an Active Lower Limb Exoskeleton

Cited by 6 publications

References 37 publications

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

3D Path Planning Based on Probabilistic Foam for UAV Indoor Applications

Optimization of Joint Space Trajectories for Assistive Lower Limb Exoskeleton Robots: Real-Time and Flexible Gait Patterns

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