Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Fast and memory-efficient multi-agent pathfinding

Abstract: Multi-agent path planning has been shown to be a PSPACE-hard problem. Running a complete search such as A* at the global level is often intractable in practice, since both the number of states and the branching factor grow exponentially as the number of mobile units increases. In addition to the inherent difficulty of the problem, in many real-life applications such as computer games, solutions have to be computed in real time, using limited CPU and memory resources. In this paper we introduce FAR (Flow Annotation Replanning), a method for multi-agent path planning on grid maps. When building a search graph from a grid map, FAR implements a flow restriction idea inspired by road networks. The movement along a given row (or column) is restricted to only one direction, avoiding head-to-head collisions. The movement direction alternates from one row (or column) to the next. Additional rules ensure that two locations reachable from each other on the original map remain connected (in both directions) in the graph. After building the search graph, an A* search is independently run for each mobile unit. During plan execution, deadlocks are detected as cycles of units that wait for each other to move. A heuristic procedure for deadlock breaking attempts to repair plans locally, instead of running a larger scale, more expensive replanning step. Experiments are run on a collection of maps extracted from BALDUR'S GATE1, a popular commercial computer game. We compare FAR with WHCA*, a recent successful algorithm for multi-agent path planning on grid maps. FAR is shown to run significantly faster, use much less memory, and scale up to problems with more mobile units.

Non-gaited humanoid locomotion planning

Abstract: This paper presents a non-gaited motion planner for humanoid robots navigating very uneven and sloped terrain The planner allows contact with any pre-designated part of the robot's body, since the use of hands or knees (in addition to feet) may be required to balance It uses a probabilistic, sample-based approach to compute each step One challenge of this approach is that most randomly sampled configurations do not satisfy all motion constraints (closed-chain, equilibrium, collision) To address this problem, a method of iterative constraint enforcement is presented that samples feasible configurations much more quickly Example motions planned for the humanoid robot HRP-2 are shown in simulation

Robust real-time lane and road detection in critical shadow conditions

Abstract: This paper presents the vision-based road detection system currently installed onto the MOB-LAB land vehicle. Based on a geometrical transform and on a fast morphological processing, the system is capable to detect road markings even in extremely severe shadow allows to achieve a processing rate of about 17 Hz.

Manipulation planning with caging grasps

Abstract: We present a novel motion planning algorithm for performing constrained tasks such as opening doors and drawers by robots such as humanoid robots or mobile manipulators. Previous work on constrained manipulation transfers rigid constraints imposed by the target object motion directly into the robot configuration space. This often unnecessarily restricts the allowable robot motion, which can prevent the robot from performing even simple tasks, particularly if the robot has limited reachability or low number of joints. Our method computes ldquocaging graspsrdquo specific to the object and uses efficient search algorithms to produce motion plans that satisfy the task constraints. The major advantages of our technique significantly increase the range of possible motions of the robot by not having to enforce rigid constraints between the end-effector and the target object. We illustrate our approach with experimental results and examples running on two robot platforms.