Remote sensors such as CCD cameras can be used for a variety of robot sensing tasks, but given restrictions on camera location and imaging geometry, task constraints, and visual occlusion it can be difficult to find viewing positions from which the task can be completed. The complexity of these constraints suggests that automated, quantitative methods of sensor placement are likely to be useful, particularly when the workspace is cluttered and a mobile robot-mounted sensor is being used to increase the sensible region, circumvent occlusions, and so forth. We describe a camera placement planner designed to produce heuristically good static viewing positions for a robot-mounted CCD camera in an experimental workcell. It can be configured to produce viewpoints for a variety of tasks such as workpiece location, inspection and modelling; feedback control by visual servoing; and task progress monitoring. The planner uses a novel probability-based global search technique to optimize a viewpoint evaluation function that heuristically combines task, camera, robot and environmental constraints. The main advances over previous work are the incorporation of kinematic accessibility and collision constraints in the viewpoint evaluation and the introduction of a search technique powerful enough to handle the resulting strong nonlinearities.