Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

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 robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.Comment: RSS 201

Pathfinding Algorithm Optimization Via Evolution

Pathfinding is a popular computer science problem in both academic research and industrial development. The objective of pathfinding is to search for a path, often the shortest path, from one location to another on a graph. Many real world applications can be considered as pathfinding problems, including motion planning, video games, logistics, and decision making. Computer scientists have proposed different algorithms to efficiently search for the shortest path. A* search algorithm is the de facto pathfinding algorithm that uses a heuristic function to determine the best action to take based on the given information. It is the most popular pathfinding algorithm due to its simplicity and efficiency. The performance of A* is heavily dependent on the quality of the heuristic function. The heuristic function determines the search speed, accuracy, and memory consumption. Hence, designing good heuristic functions for specific domains becomes the primary research focus on pathfinding algorithm optimization. In this dissertation, we address and solve several commonly known challenges in pathfinding problems and A* algorithm. First, designing new heuristic functions is a difficult and time-consuming task, especially when they are used to solve complex problems. The task requires the user to have expert knowledge of the problem. Moreover, a single heuristic function might not be enough to digest all the provided information and return the best guidance during the search. Previous works suggest that multiple heuristics for complex problems can dramatically speed up the search. However, choosing the appropriate combination of heuristic functions is tricky. Current optimization approaches rely on hand-tuning the parameters via trial and error by engineers over many iterations. There is a need to reduce the difficulty of designing heuristic functions for search performance maximization. Our first contribution is to propose an improved A* with a self-evolving heuristic function named Evolutionary Heuristic A* (EHA*) that reduces engineering effort to design the heuristic function for A* and maximize the search performance. Our experiment results show that EHA* (i) preserves path optimality; (ii) is not limited to a particular application; (iii) speeds up the path searching process; and (iv) most importantly, dramatically reduces the difficulty for software engineers to design heuristic functions for A* search. Moreover, our work can be applied to other existing works on the performance improvement of A* search. Search, A* search suffers from poor performance on large search spaces. Although EHA* improves the quality of heuristic functions, large search space still leads to many unnecessary searches. Our second contribution is Regions Discovery Algorithm (RDA), a map clustering technique to partition a grid based map into different categories to reduce search spaces and increase search speed. Our approach reduces the size of search spaces by partitioning a graph into many segments and identifying the segments by their characteristics. By identifying segments in different categories, we can easily eliminate search space, such as rooms, that are not possible (better use needed?) to be part of the optimal solution. Unlike the existing approaches that might result in non-optimal solutions, our experiment results show that RDA guarantees optimal solutions. Our third contribution, the Hierarchical Evolutionary Heuristic A* (HEHA*), further improves the search ability of handling complex pathfinding problems and boosting the search performance, by reducing search spaces and exploiting parallelism techniques. HEHA* combines the strength of EHA* and RDA to reduce search spaces and improve search speed. HEHA* shows that it provides better search performance with less memory consumption. In the pre-processing phase, first HEHA* partitions a graph into different segments and then applies different optimized heuristic functions for each segment to maximize the search performance. During the online process, HEHA* searches on the abstract level first to reduce search area, and exploits parallelism to speed up the search. Fourth, we improve and apply HEHA* to Multi-Agent Pathfinding (MAPF) problems. MAPF is the fundamental problem of many robotic and logistic applications, where the main constraint is that all agents can find the shortest paths while not colliding with each other. While the current trend favors the central controlled system, our approach is to develop a distributed version of HEHA* that can efficiently plan the optimal path for each agent. Such a system requires data sharing and exchanging among the agents, so that each agent can make its own decision without a supervising system. Our experiment results show that the Multi-Agent version of HEHA* maintains a high success rate when the number of agents increases. While EHA* and HEHA* provide a novel approach for heuristic function design, the pre-processing times are not trivial. To boost the performance of the preprocessing steps in EHA* and HEHA*, we propose a FPGA-based reconfigurable hardware accelerator that is not bound to any specific applications as our fifth contribution. Since GA requires many independent processes, it is suitable to implement it in a hardware accelerator to gain maximum performance. We apply the following techniques to enhance performance: deep pipelining, reconfigurable computing, massive parallel processing, and degree of parallelism maximization. Our results show that the FPGA accelerator for EHA* improves the scalability, throughput, and latency

Efficient Motion and Inspection Planning for Medical Robots with Theoretical Guarantees

Medical robots enable faster and safer patient care. Continuum medical robots (e.g., steerable needles) have great potential to accomplish procedures with less damage to patients compared to conventional instruments (e.g., reducing puncturing and cutting of tissues). Due to their complexity and degrees of freedom, such robots are often harder and less intuitive for physicians to operate directly. Automating robot-assisted medical procedures can enable physicians and patients to harness the full potential of medical robots in terms of safety, efficiency, accuracy, and precision.Motion planning methods compute motions for a robot that satisfy various constraints and accomplish a specific task, e.g., plan motions for a mobile robot to move to a target spot while avoiding obstacles. Inspection planning is the task of planning motions for a robot to inspect a set of points of interest, and it has applications in domains such as industrial, field, and medical robotics. With motion and inspection planning, medical robots would be able to automatically accomplish tasks like biopsy and endoscopy while minimizing safety risks and damage to the patient. Computing a motion or inspection plan can be computationally hard since we have to consider application-specific constraints, which come from the robotic system due to the mechanical properties of the robot or come from the environment, such as the requirement to avoid critical anatomical structures during the procedure.I develop motion and inspection planning algorithms that focus on efficiency and effectiveness. Given the same computing power, higher efficiency would shorten the procedure time, thus reducing costs and improving patient outcomes. Additionally, for the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the algorithms involved in procedure automation. Therefore, I focus on providing theoretical guarantees to certify the performance of planners. More specifically, it is important to certify if a planner is able to find a plan if one exists (i.e., completeness) and if a planner is able to find a globally optimal plan according to a given metric (i.e., optimality).Doctor of Philosoph