Falco: Fast likelihood‐based collision avoidance with extension to human‐guided navigation

Ji, Zhang; Chen, Hu; Chadha, Rushat Gupta; Singh, Sanjiv

doi:10.1002/rob.21952

Cited by 57 publications

(29 citation statements)

References 28 publications

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“…The custom robot is developed for simulation studies assuming a quadrotor 350 mm MAV model with a laser scanner, a 2D rotating scanner up to 20 m. The local planning window is set to 5 m Â 5 m Â 5 m around the current vehicle pose, and the flight speed was set to 2 m/s. In all simulated tests the performance comparison is conducted with receding horizon rapidly-exploring random tree * (RHRRT * ) [34] and likelihood-based planning [35]. Simulation results presenting the performance of the (a) proposed planner, (b) Falco, and (c) RHRRT * planner are shown in Figures 6, 7, and 8.…”

Section: Simulation Resultsmentioning

confidence: 99%

Collision‐free local planner for unknown subterranean navigation

et al. 2021

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When operating in confined spaces or near obstacles, collision-free path planning is an essential requirement for autonomous exploration in unknown environments. This study presents an autonomous exploration technique using a carefully designed collision-free local planner. Using LiDAR range measurements, a local end-point selection method is designed, and the path is generated from the current position to the selected end-point. The generated path showed the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The results consistently demonstrated the safety and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flights in environment with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial robot systems. In addition, our drone performed an autonomous mission in the tunnel circuit competition (Phase 1) of the DARPA Subterranean Challenge.

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Section: Simulation Resultsmentioning

confidence: 99%

Collision‐free local planner for unknown subterranean navigation

et al. 2021

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“…To understand the nature of the PROMPT planning framework and its efficacy to current state-of-the-art planners, we compare our planners (PROMPT-O, PROMPT-S) with two classes of planners: search based discrete motion primitives planner FALCO( [25]) and trajectory optimisation based planners STOMP( [7]) and TEB( [20]). While there are numerous variants to these planners, these three have been chosen due to their demonstrated improved performance over others.…”

Section: Methodsmentioning

confidence: 99%

“…Recent work on discrete motion primitives, [25], introduces path groups as a mechanism to couple a set of deterministic trajectories whose combined quality (in terms of likelihood of collision free path) enables robust trajectory planning for partially known environments. In essence our approach takes a probabilistic approach of representing such trajectory bundles as Gaussian radial basis functions (GRBFs).…”

Section: Introductionmentioning

confidence: 99%

PROMPT: Probabilistic Motion Primitives based Trajectory Planning

Loew¹,

Bandyopadhyay²,

Williams³

et al. 2021

Robotics: Science and Systems XVII

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We present a novel approach to motion planning for autonomous ground vehicles by formulating motion primitives as probabilistic distributions of trajectories (aka probabilistic motion primitives -ProMP) and performing stochastic optimisation on them for finding an optimal path. We show that compared to the traditional approach of using discrete motion primitives or direct stochastic optimisation of the whole path, incorporating ProMPs enables higher quality of paths by enabling constraint conditioning, combination and blending of probability distributions. We present two motion planners utilizing this approach: feasibility based trajectory sampling (PROMPT-S) and stochastic gradient-based trajectory optimisation (PROMPT-O). We show simulation results of our approach outperforming state of the art optimisation as well as discrete motion primitives based planners.We additionally illustrate the versatility of our approach by showing PROMPT's ability to handle significantly skewed motion primitives, e.g, as induced by steering failure in AGVs as well as composition of motion primitives to perform complex manoeuvres. Finally, we demonstrate the practicality of these planners by implementing them on a real self-driving vehicle navigating on structured and unstructured off-road terrains.

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“…Then, the mapping module stitches sensor measurements concerning their associated poses and reconstructs the surroundings. Finally, based on the reconstructed map, the motion planning module generates obstacle-awareness and smooth trajectories [14], [15]. The mapping module in the navigation work bridges the gap between localization and planning modules and provides a human-readable map to the system monitor.…”

Section: Relative Workmentioning

confidence: 99%

Multilayer Mapping Kit for Autonomous UAV Navigation

Chen

Chang

et al. 2021

IEEE Access

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Mapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping framework. In this framework, we divided the map into three layers: awareness, local, and global. The awareness map is constructed in cylindrical coordinate, enabling fast raycasting. The local map is a probability-based volumetric map. The global map adopts dynamic memory management, allocating memory for the active mapping area, and recycling the memory from the inactive mapping area. We implemented this mapping framework in three parallel threads: awareness thread, local-global thread, and visualization thread. Finally, we evaluated the mapping kit in both the simulation environment and the realworld scenario with the vision-based sensors. The framework supports different kinds of map outputs for the global or local path planners. The implementation is open-source for the research community.

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Falco: Fast likelihood‐based collision avoidance with extension to human‐guided navigation

Cited by 57 publications

References 28 publications

Collision‐free local planner for unknown subterranean navigation

Collision‐free local planner for unknown subterranean navigation

PROMPT: Probabilistic Motion Primitives based Trajectory Planning

Multilayer Mapping Kit for Autonomous UAV Navigation

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