Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Ren, Kai; Ahn, Heejin; Kamgarpour, Maryam

doi:10.1109/lcsys.2022.3186269

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…Uncertainties can impact the ability to work in a contact‐rich environment. There are two approaches to addressing uncertainties: continuously sensing and correcting [38, 39], or planning trajectories that are robust to various scenarios [40, 41].…”

Section: Related Workmentioning

confidence: 99%

Compliant‐based robotic 3D bin packing with unavoidable uncertainties

Shuai

Gao

et al. 2023

IET Control Theory & Appl

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Robotic 3D bin packing (R‐3dBPP), aiming to place deformed cases in various sizes in the container without fences, is a comprehensive application that includes perception, planning, execution, and hardware design. Traditional studies assume that the context of the real space must be accurately perceived and represented. However, disjunctions between the planner and reality are unavoidable in R‐3dBPP, especially with low‐cost sensors. As far as the author knows, there is no practical solution. In this paper, the above assumption is discarded and the typical types of uncertainties prevalent to guide the design of the algorithms are formulated. A new online bin‐packing algorithm is proposed, keeping deformed boxes stacked in close contact with each other, so that the whole pallet is kept stable by friction between the boxes and the container. In order to meet the requirement of close contact under the non‐negligible errors from sensors and planners, a compliant‐based motion planning system is introduced. It replaces the precision‐based feedback with a compliant end‐effector and accompanying motion strategies. Last but not least, a complete online bin‐packing robot system is developed and the system's performance under the influence of the uncertainties mentioned above through simulation and physical experiments is evaluated.

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Section: Related Workmentioning

confidence: 99%

Compliant‐based robotic 3D bin packing with unavoidable uncertainties

Shuai

Gao

et al. 2023

IET Control Theory & Appl

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“…For risk-aware planning, CVaR has been applied to avoid various risks such as moving obstacles in robot navigation [24], [25], uncertain regions in semantic segmentation of road scenes [26], or an abstract risk in a hazardous process [27]. While these studies focus on either collision avoidance [24], [25], classification-based traversability modeling [26], or a single-modal risk behavior [27], our focus is on handling the immobilization risk that requires continuous and multi-modal modeling of rover slip on various kinds of deformable terrains. Risk-aware off-road navigation by Cai et al [28] uses CVaR to estimate pessimistic robot speed in environments with dirt and vegetation.…”

Section: Related Workmentioning

confidence: 99%

Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

Endo

Ishigami

2022

IEEE Robot. Autom. Lett.

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Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.

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“…In a wearable sensor network (WSN), optimal capacity analysis is needed for energy conservation. Multi-radio-multichannel WSN intrinsically demands mixed integer program multi-radio formulation [50]. This is an NP-hard problem found in most recent heuristic algorithms such as the Gaussian mixture model (GMM), and multi-dimensional conflict graph (MDCG).…”

Section: Multi-dimensional Graph-based Computingmentioning

confidence: 99%

WearROBOT: An Energy Conservative Wearable Obstacle Detection Robot With LP Multi-Commodity Graph

Okafor

Longe

2022

IEEE Access

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Human activity wearable obstacle detection for the visually impaired (VI) is developed for routine monitoring and observation of surrounding events. Environmental observation, home surveillance, and assistive supports are now built on wearable devices using Inertia-based sensors, such as accelerometers, linear acceleration, and gyroscopes. Previous assisted living system (ALS) still faces challenges in energy management and resource allocation when performing daily activities, particularly with ambulation. Legacy systems cannot fully improve self-esteem, hence, WearROBOT. It detects rearview obstacles and has an audio feedback system incorporated for voicing out once an obstacle is detected. Linear programing (LP) multi-commodity graph (LMCG) learning model is proposed while coupling the shortest path resource allocation for space diversity linearization. An Infrared sensor problem function that minimizes link utilization is derived. Angle-Intensity analysis (AIA) is carried out on various use case scenarios to enable the user to know the best angle to consider depending on its usage and battery conservation. The work showed how intensity differs at various angles at 5°,15°,20° 35°, and 45° respectively. Also, the reflectivity of different materials and how it affects the battery life are studied. As the wearable robot moves away from the node-obstacle, the LMCG narrow-band sensor node (LMCG-NB-IoT) drops energy significantly. The Low Power WAN (LP-WAN), Bluetooth Low-Energy (BLE) and proposed LMCG-NB-IoT offered 51.28%, 33.33%, and 15.39% respectively. In terms of energy latency, the schemes gave 65.63%, 31.25%, and 3.12% respectively. Similarly, the proposed LMCG-NB-IoT had a 50% battery life profile. Finally, WearROBOT mobility aid minimizes injuries experienced by the visually impaired.

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Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Cited by 16 publications

References 24 publications

Compliant‐based robotic 3D bin packing with unavoidable uncertainties

Compliant‐based robotic 3D bin packing with unavoidable uncertainties

Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

WearROBOT: An Energy Conservative Wearable Obstacle Detection Robot With LP Multi-Commodity Graph

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