28 citations · 143 across the 25 of their papers we have counts for
27 papers
DigiArm: An Anthropomorphic 3D-Printed Prosthetic Hand with Enhanced Dexterity for Typing Tasks
Dean Zadok, Tom Naamani, Yuval Bar-Ratson +5
Despite recent advancements, existing prosthetic limbs are unable to replicate the dexterity and intuitive control of the human hand. Current control systems for prosthetic hands a…
Generalizing Multi-Objective Search via Objective-Aggregation Functions
Hadar Peer, Eyal Weiss, Ron Alterovitz +1
Multi-objective search (MOS) has become essential in robotics, as real-world robotic systems need to simultaneously balance multiple, often conflicting objectives. Recent works exp…
Incremental Generalized Hybrid A*
Sidharth Talia, Oren Salzman, Siddhartha Srinivasa
We address the problem of efficiently organizing search over very large trees, which arises in many applications ranging from autonomous driving to aerial vehicles. Here, we are mo…
Resolution-Optimal Motion Planning for Steerable Needles
Mengyu Fu, Kiril Solovey, Oren Salzman +1
Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically sign…
Toward Certifiable Motion Planning for Medical Steerable Needles
Mengyu Fu, Oren Salzman, Ron Alterovitz
Medical steerable needles can move along 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steera…
Computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search
Mengyu Fu, Oren Salzman, Ron Alterovitz
The inspection-planning problem calls for computing motions for a robot that allow it to inspect a set of points of interest (POIs) while considering plan quality (e.g., plan lengt…
Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model
Michael Bentley, Caleb Rucker, Chakravarthy Reddy +2
Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the p…
Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search
Mengyu Fu, Alan Kuntz, Oren Salzman +1
Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robo…
Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles
Aditya Mandalika, Sanjiban Choudhury, Oren Salzman +1
Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in t…
Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges
Aditya Mandalika, Oren Salzman, Siddhartha Srinivasa
Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the…
Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations
Shushman Choudhury, Oren Salzman, Sanjiban Choudhury +2
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are co…
Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo
Daqing Yi, Rohan Thakker, Cole Gulino +2
Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found,…
The Provable Virtue of Laziness in Motion Planning
Nika Haghtalab, Simon Mackenzie, Ariel D. Procaccia +2
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target. These algorithms were desi…
An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles
Pankaj K. Agarwal, Kyle Fox, Oren Salzman
We study a path-planning problem amid a set of obstacles in , in which we wish to compute a short path between two points while also maintaining a high…
Efficient motion planning for problems lacking optimal substructure
Oren Salzman, Brian Hou, Siddhartha Srinivasa
We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized i…
Open problem on risk-aware planning in the plane
Oren Salzman, Siddhartha Srinivasa
We consider the problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear…
Densification Strategies for Anytime Motion Planning over Large Dense Roadmaps
Shushman Choudhury, Oren Salzman, Sanjiban Choudhury +1
We consider the problem of computing shortest paths in a dense motion-planning roadmap . We assume that~, the number of vertices of , is very large. Th…
Collision detection or nearest-neighbor search? On the computational bottleneck in sampling-based motion planning
Michal Kleinbort, Oren Salzman, Dan Halperin
The complexity of nearest-neighbor search dominates the asymptotic running time of many sampling-based motion-planning algorithms. However, collision detection is often considered…
New perspective on sampling-based motion planning via random geometric graphs
Kiril Solovey, Oren Salzman, Dan Halperin
Roadmaps constructed by many sampling-based motion planners coincide, in the absence of obstacles, with standard models of random geometric graphs (RGGs). Those models have been st…
Motion Planning for Multi-Link Robots by Implicit Configuration-Space Tiling
Oren Salzman, Kiril Solovey, Dan Halperin
We study the problem of motion-planning for free-flying multi-link robots and develop a sampling-based algorithm that is specifically tailored for the task. Our work is based on th…
Efficient high-quality motion planning by fast all-pairs r-nearest-neighbors
Michal Kleinbort, Oren Salzman, Dan Halperin
Sampling-based motion-planning algorithms typically rely on nearest-neighbor (NN) queries when constructing a roadmap. Recent results suggest that in various settings NN queries ma…
Asymptotically-Optimal Motion Planning using Lower Bounds on Cost
Oren Salzman, Dan Halperin
Many path-finding algorithms on graphs such as A* are sped up by using a heuristic function that gives lower bounds on the cost to reach the goal. Aiming to apply similar technique…
Asymptotically near-optimal RRT for fast, high-quality, motion planning
Oren Salzman, Dan Halperin
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a s…
Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning
Kiril Solovey, Oren Salzman, Dan Halperin
We present a sampling-based framework for multi-robot motion planning which combines an implicit representation of a roadmap with a novel approach for pathfinding in geometrically…